Editors: Catherine Chronaki, EFMI, Prof. Zhi Yang, Capital Medical University
In Memory of Ragnar Norbert 1936-2020
Saturday October 17, 2020: Preconference Tutorials
PT1: Medical Robotics: Design Considerations and Research Tools (医疗机器人：设计方针及研究工具)
08:00-09:30 Brussels / 14:00-15:30 Beijing
The structure of this tutorial will be as follows:
- General Overview
- Research Progress (History, Examples, Current Stage, Challenges)
- Robot configurations (Classical serial arm, Non-classical serial arm, Parallel arm, Passive/active arm, Continuum robot, Soft robot, Hybrid system)
- Robot Design (Actuator, Mechanical transmission, Microcontroller, 3D printing, Example Tools: CAD software, Misumi Platform, Arduino, Raspberry Pi, and etc.)
- Force Sensing (Principles, Examples, Applications)
- Control (Kinematics, Human-robot interactions, Force & position control, Active & Passive compliance, Example Tools: CoppeliaSim, ROS, Matlab toolbox)
- Pre-planning and Navigation (Segmentation, Registration, Pre-planning, Tracking, Calibration, Example Tools: ITK-SNAP, 3D Slicer, VTK, PLUS, and etc.)
- Safety (Industrial v.s. Medical, Human factor, Clinical constrain, Rules: European & China, Strategies, Example Tools: Hazard traceability & risk evaluation matrix, FMECA/P-FMECA)
Speaker: Shuangyi Wang, Associate Professor, Chinese Academy of Sciences, Institute of Automation (CASIA)
Dr. Shuangyi Wang received his PhD from King’s College London (KCL) on developing the world’s first robotic trans-esophageal ultrasound system. He continuously worked as a Post-doctoral researcher in KCL on developing advanced intelligent fetal diagnostic robots. In 2019, he was appointed as the Associate Professor at the Chinese Academy of Sciences, Institute of Automation (CASIA) and supported by the CAS Talents grants. He has been working on the projects funded by the EPSRC, Wellcome Trust, HTC, NSFC across UK and China and took the leading role in developing several robotic systems used in different medical scenario. Meanwhile, he has been actively involved in scientific research and published research papers on medical robotics in top journals and conferences, e.g. IEEE RA Magazine, IEEE TBME, Micromachines, MICCAI, IROS, TAROS. His current research interests include medical robotics, robotic ultrasound, computer-assisted interventions, and robotic-assisted diagnosis. Watch his presentation here:
PT2: Findable Accessible Interoperable Reusable (FAIR) Health Data Sets with HL7 FHIR: New Standards to support Health Research
10:00-11:30am Brussels /16:00-17:30 Beijing
This session will introduce FAIR health data sets and associated maturity models. Attendees will learn about measuring the degree of Findability, Accessibility, Interoperability, and Reusability (FAIR). After introduction to Data FAIRness and the FAIR4Health Project, the FAIRness indicators under the FAIR maturity model developed by RDA will be presented. Then, the FAIRification tools developed in the FAIR4Health project used for distributed data mining will be demonstrated. Finally, the emerging FHIR standards supported by the HL7 exploratory project on HL7 FHIR ways to deliver FAIR health data sets, “FAIRness for HL7 FHIR” will be presented with an invitation to participate and contribute. The session is organized by FAIR4Health project and supported by EFMI, RDA, and HL7 Europe.
- Learn about FAIR health data sets and the FAIR4Health Project (www.fair4Health.eu)
- Experience the use of FAIRification tools in health care
- Learn about the Reseach Data Alliance and the FAIR maturity model
- Be informed about the FAIR4FHIR HL7 standards project and how to participate.
Watch the presentation here:
Catherine Chronaki is a computer engineer, vice president and president elect (2020-2022) European Federation for Medical Informatics, Secretary General HL7 Foundation. Catherine has played a key role within National and European digital health projects. Author of 100+ research papers, she has served as Associate Editor IEEE TITB, and on major eHealth conferences. Catherine served the HL7 Board (2008-2012), the eCardiology WG, European Society of Cardiology (2012-2015), and the eHealth Stakeholders group of the European Commission (2013-2022). She was coordinator of the Trillium Bridge and Trillium II on projects advancing adoption of international patient summary standards and eStandards that delivered a standardization roadmap for large scale eHealth deployment in Europe. She is involved in the FHIR4Health project contributing to the certification roadmap from HL7 FHIR perspective. She will moderate the session.
Esther Thea is a nurse-midwife and a PhD student in Medical Informatics (FAIR data principles; evaluating the FAIRness of health data) at the Universitätsmedizin Greifswald. She is visiting researcher at IMISE University of Leipzig, Germany Leipzig, where her work is focussed on testing the FAIR4Health platform from the point of view of client functionality and usability. She completed BSc on Nursing and Public Health at the Kenyatta University in Kenya and continued her master studies in Medical Informatics in Germany. She is coordinator for the European Open Science Cloud COVID-19 Archives project that aims to disseminate simulation studies of COVID‐19 models to the research community as COMBINE archives, and provide fully featured, reproducible COMBINE archives for COVID models and disseminates COVID modelsShe has experience in evaluating the FAIRness of data quality frameworks for medical data. Esther will introduce the concept of FAIR data.
Alicia Martínez García, PhD. She is a software engineer, researcher at the Institute of Biomedicine of Seville, and executive co-coordinator for the FAIR4Health project at the Andalusian Health Service. FAIR4Health aims to facilitate and encourage the EU Health Research community to apply the FAIR principles, share and reuse datasets derived from publicly funded research initiatives by demonstrating the potential impact on health outcomes and health research. Alicia participates in national and international research projects on interoperability, software engineering, clinical decision support tools, health informatics standards, FAIR principles, and model-driven engineering. She is member of CTN139 committee in AENOR and RDA (Research Data Alliance). Alicia will introduce the FAIR4Health project.
Anil Sinaci is a computer scientist and full-stack software developer working as a principal solutions architect at SRDC Corp. in Ankara, Turkey. He has 10+ years of development experience ranging from ordinary desktop and web applications to cutting-edge research projects. He holds a PhD in Computer Engineering; his research areas include eHealth and eBusiness Infrastructures, Clinical Research Informatics, Data Interoperability, Semantic Web, Service Oriented Architectures, Interoperability Standards, and Conformance and Interoperability Testing. He has actively worked for many European Commission supported research projects such as FAIR4Health, Medolution, POWER2DM, SALUS, IKS, BIVEE, iSURF and HAGRID. He authored many papers published in peer-reviewed journals and conferences. Anil will introduce the FAIR4Health FAIRification tools.
Mert Gençtürk is a computer scientist and full-stack software developer working as a technical manager and researcher at SRDC Corp. in Ankara, Turkey. His research areas include eHealth (national eHealth infrastructures, EHR, PHR, data exchange standards), mHealth, Interoperability, Semantic Web, Data Mining, Machine Learning, Security and Privacy. He has participated in several large-scale R&D projects including PALANTE, C2-SENSE, and FAIR4Health supported by the European Commission. He holds a Master’s degree in computer engineering, and conducts his PhD study on development of a novel federated machine learning methodology for privacy-concerned environments in the FAIR4Health Project. Mert will demonstrate FAIR4Health FAIRification tools.
Oya Beyan is a researcher at Fraunhofer Institute for Applied IT and RWTH Aachen University. Her research focuses on methods of data reusability and FAIR data, data-driven transformation and distributed analytics. Her area of expertise is in the semantic web technologies and their application in health care and life sciences. She actively contributes to the national and international initiatives to enable adoption of FAIR principles and develops tools and infrastructures supporting FAIR data. With her interdisciplinary background in informatics, medical informatics and sociology, she focuses on societal reflections of data-driven change. In FAIRplus, Oya contributes to a FAIR Maturity Model to guide organizations to improve their data processes delivering reusable and machine actionable data. Oya will introduce RDA and the FAIR maturity model.
Giorgio Cangioli is technical lead of HL7 Europe and the HL7 FAIR4FHIR Project. Senior Consultant of ICT in Health and Social Care, Degree in Physics, PhD in Energy Engineering, Master in ICT for Radiology. Giorgio has worked in the private sector as Production Manager, QMS Responsible, and R&D Responsible, and has extensive experience in ICT, standards and business process reengineering in health and social care. Giorgio is involved in several telemedicine; teleradiology, social care, primary care, Health Information Exchange, and eGovernment national and European. He assessed Regional eHealth projects for an Italian governmental agency. Giorgio led the epSOS Clinical and Semantic Experts Group and was the Trillium Bridge project manager. He served the HL7 Technical Steering Committee (2012; 2014-2015), authored of DICOM Supp 88, Chair of HL7 Italy and. Giorgio will provide and update on the FAIR4FHIR project and will explain how to participate and contribute.
PT3. Healthcare Innovation in the Post Pandemic Era
12:00-13:30 Brussels /18:00-19:30 Beijing
Today we are facing one of the worst pandemics globally. COVID-19 impacted and changed almost every aspect of our live. In healthcare industry, although some businesses had growth, most of businesses are facing challenges in negative ways. While many countries are still in an economic contraction, China has managed to control the spread of the virus and started a rapid economic recovery. In China, we have seen that the recovery on the supply side is faster than on the consumer side. In near term our healthcare industry might be facing contractions in both domestic consumption and exporting. This pandemic also brought many challenges in industrial supply chain, with a trend of technological de-collaboration and business decoupling. To overcome these challenges in the post-pandemic era, continuous innovation is the only way for healthcare business survival and prosper.
This lecture will discuss the key elements in healthcare innovation, and provide examples how Beijing Economic-Technological Development District supports innovation efforts from entrepreneurs and businesses.
Speaker: Professor Shanhong Mao, Capital Medical University
Dr. Mao is a professor and PhD supervisor (part time) at Biomedical Engineering School of Chinese Capital Medical University. He also serves Chief Counsellor and Visiting Fellow of Chinese Academy of Inspection and Quarantine Sciences. With more than 30 years of experiences in healthcare innovation and business development, Dr. Mao served as Global Head of Mfg. Science and Technology at Alcon (Novartis), and various of executive positions in fortune 500 companies e.g. Bausch&Lomb and 3M. His expertise spans pharmaceuticals, medical devices, artificial intelligence in healthcare, and material science. Dr. Mao also served as adjunct professor at UTA, there he created the “Innovation and New Product Development” course to teach UT students new product development skills encompassing healthcare market analysis and segmentation, crossing innovation “valley of death” , product concept development, product design, mfg. process design and development, preclinical and clinical studies, regulatory approval, IP, business plan and project management. This class is brought to CCMU and currently taught at graduate student level. Dr. Mao has 15 patents, and 40 peer reviewed papers. He received his MBA from Carlson School of Business at University of Minnesota, PhD from UC Berkeley, MSc from Tsinghua University and BA from Peking University. Watch his presentation here:
Tuesday October 20, 2020: Nobel in Medicine and other Workshops
Will the next Nobel prize be in preventive medicine with quantum computing?
08:00 – 08:30 Brussels CET/14:00-14:30 Beijing
How should we spend our resources from a Healthy-Risk-Ill – perspective? Should we spend our AI/ML projects on diagnosis and treatment or prediction and prevention? We will talk about a system transformation where we spend more than 2.8% (OECD) on prediction and prevention with the use of quantum computing capacity.
Speaker: Ebba Carbonnier, Swelife and Karolinska Institute
Ebba Carbonnier Portfolio Manager for Nationally Scalable Solutions at Swelife, a Strategic Innovation Programme within Life Science. Examples of some of the national projects within the portfolio are Cell- and Gene Therapy, Biobanking and Prevention of childhood obesity. Carbonnier was previously KI’s Programme Leader for a joint programme between KI and Stockholm County Council. The programme aimed to improve conditions for healthcare and research to enable the rapid transfer of knowledge to personalized prevention and treatment. Key components in the solutions delivered were semantic interoperability, standardized outcome measures and digitization. The programme encompassed five projects and 25 subprojects within the areas of Arthritis, Breast cancer, Diabetes and Heart Failure. Prior to KI, Carbonnier has 14 years of Management Consulting experience, ranging from daily management of teams to creating and implementing strategies for global companies. Managing large scale multinational projects at e.g. AstraZeneca, Microsoft and Sandvik, is an area where Carbonnier has extensive experience. Carbonnier holds an MSc and an MBA with a focus on Operations Management. Having lived/studied/worked in five countries apart from Sweden, has given Carbonnier an ability to handle situations and create results in complex environments. Carbonnier is also a guest lecturer at the Swedish Royal Institute of Technology (KTH) within System Engineering. Carbonnier, Göran Johansson and Per Sikora initiated a collaboration between WACQT, GMS and Swelife in the beginning of 2019 with the purpose of starting to identify Quantum Algorithms suitable for Metagenomics within Life Sciences. Focus in the long run is on Preventative Health in a Healthy-Risk-Ill perspective so that we do not merely go on spending 97% of resources in the healthcare sector on the Ill part. Watch her presentation here:
Workshop: Clinician’s perspective on the merit of medical imaging
08:30-10:00 Brussels/14:30-16:00 Beijing
Automated software on perfusion images in acute ischemic stroke,
Automated imaging software is integral to decision-making in acute ischemic stroke (AIS) during extended time windows. RAPID software is the most widely used and has been validated in landmark endovascular trials. Olea software is another commercially-available and FDA-approved software, but has not been studied in AIS trials. Both softwares are not certificated in China. We aimed to develop and validate an automated software based on perfusion weighted imaging, and compare the accuracy of the new software in everyday clinical practice outside of a clinical trial.
Speaker: Yunyun Xiong, MD, PhD, Associate chief physician, Associate professor, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University
- Fudan Medical University, MD, MSc（2008）
- The Chinese University of Hong Kong, PhD, Post-doc（2011）
- BIDMC, Harvard University, Visiting scholar （2019）
Now she is associate chief physician and associate professor in Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, editorial board member in BMC neurology. Her research interest is the reperfusion therapy for acute ischemic stroke. She has published 77 SCI papers, and held two national grants from the National Natural Science Foundation of China. Watch her presentation here.
Application of MR enterography in small bowel disease
With the development of imaging technology in recent years, CT enterography and MR enterography have increasingly become the major modalities of gastrointestinal diseases. In addition, due to non-radiation danger and super soft tissue contrast, MRE becomes more and more preferable. Diagnosis for bowel disease is a challenge owing to nonspecific clinical manifestation, rare occurrence, and low index of clinical suspicion. Yet, various disease including both neoplasm and non-neoplasm have characteristic imaging features at MRI when optimal distention of the small bowel is achieved, correlating well with features seen in gross specimens. Understanding the imaging features of small bowel disease is important to improve the radiologist’s ability. In addition, with the rapid development of MR techniques, MRE could provide more and more information beyond diagnosis.
Speaker: Dr. Jing Liu, MD, PhD, Associate chief physician, Radiologist in Peking University First Hospital
She is engaged in the diagnosis and research work related to magnetic resonance imaging (MRI), and her main research direction is gastrointestinal MR imaging and the influence of diseases on brain functional MRI. In 2016, she was awarded the Sino German “Spark Program” of the Chinese society of Radiology and went to Mannheim Medical College in Germany for further study. At present, about 13 papers have been published in journals of SCI as the first author and/or corresponding author. The cumulative impact factor is about 37 points. She presided over one National Natural Science Foundation and one Beijing Natural Science Foundation. She was awarded the advanced individual award of the first hospital of Peking University, the star of scientific research in the first hospital of Peking University, the excellent night university teacher of the medical department of Peking University, the outstanding young teacher of the first hospital of Peking University. Watch her presentation here:
Automated software on perfusion images in acute ischemic stroke,
Automated imaging software is integral to decision-making in acute ischemic stroke (AIS) during extended time windows. RAPID software is the most widely used and has been validated in landmark endovascular trials. Olea software is another commercially-available and FDA-approved software, but has not been studied in AIS trials. Both softwares are not certificated in China. We aimed to develop and validate an automated software based on perfusion weighted imaging, and compare the accuracy of the new software in everyday clinical practice outside of a clinical trial.
Wednesday October 21, 2020: Day #1 – Health Informatics
Welcome speech, Vice PresidenJi Xunming of Capital Medical University
Dedication to Ragnar Nordberg (1936-2020)
This is the Obituary Testimony of Ragnar Nordberg born 1936 and died at the age of 84 years ,9th of October this year. The Sea was important for Ragnar, and the freedom to sail without seeing any borders. And that describes Ragnar, He were able to see further than many others, he found always a way how to cross a border and his eyes were always lifted to the knowledge horizon. Finishing his PhD in atomic physics at the age of 32 in the field of Electron spectroscopy. At Uppsala University. His supervisor Professor Kai Siegbahn got later the Nobel prize in 1981, some of the ground work were done by RagnarThe year 1968-1969 he spent in USA (Hewlett Packard) he turned some of his ideas to patents, and work closely with Hewelett Packard. The store tells that he has a letter from a young Bill Gates that wrote to Ragnar and applied to be a member of the HP User club, that Ragnar was the Chair of 1970 back to Sahlgrenska University hospital, were he became the operational manager for the Clinical Chemical Central Laboratory and ended up as the IT Director of the hospital 1990 until his retirement 2001. His interest for developing IT Systems and have them to talk with eatch other, lead to representation as a Swedish delegate in CEN TC251 WG 3 and ISO TC 215 WG4 1997 – 2010. But we all remember Ragnar on the frontline on sharing knowledge, not only for the academic domain, but how technology could support the professional’s workday. His engagement in the Swedish association that he was one of the founders of in the beginning of the 70ties has been important for the curriculum in Informatics in Sweden. Local Chair of MIE 2008 and later my supervisor in MIE 2018. Engaged in EFMI and functioned as EFMI´s treasurer from 2015 to 2019. Ragnar’s capacity to connect people and build networks between them was remarkable. Therefore, I would like to dedicate this EU-China Summit to the memory of Ragnar Nordberg. Watch the Obituary Testimony of Ragnar Nordberg here:
Lars Lindsköld, Swelife
Med.Dr Lars Lindsköld, Portfolio manager Swelife, leads the Sweper project – a Swedish national initiative to support the usage of systematic health data in personalized medicine. Senior lecture Informatics, Department of Applied Information Technology, Gothenburg University, Regional developer, Healthcare Head Office, Region Västra Götaland, Senior adviser AI Innovation of Sweden and Secretary Swedish Association of Medical Informatics, and from November 2020 also Institutional member officer EFMI. Med. Dr. Lindsköld has a long digitalization experience of Radiology, Pathology, and Teledermatology (e-Health). His research is based on Interoperability within big data and AI with a focus on Semantic Interoperability driven by profession. Today working together with the medical profession to create a Medical Machine-Readable Lexicon that will increase the automatic portability of data from various IT systems to support better the journey of the disease that will include actors as the individuals, clinician’s, researcher and industry. The need for data increases enormously as many factors, each with a marginal influence, builds the basis for personalization. (Systematic Health data). In chronic and cancer disorders, it is specifically vital that the data travel with the patient and continuously extract structured data to continuously feed relevant information to the decision space and faster scale-up solutions to benefit the society. Done with the companies involved in the transformation of healthcare.
Opening Keynote: Evaluating EHR Adoption across China’s Hospital
21.10.2020 08:30-09:00 Brussels/ 14:00-14:30 Beijing
The motivation for installing EHR in China’s hospitals was reviewed. The Model of EHR Grading (MEG) was used to assess the level of EHR adoption across mainland China’s hospitals. MEG defines 39 EHR functions (e.g., order entry) which are grouped by 10 roles (e.g., inpatient physicians) and grades each function and the overall EHR adoption into nine levels (0–8). Besides the function factor, the EHR usage ratio and the data quality factors are also included in the assessment. The evaluating data across mainland China’s hospitals from 2011 to 2019 was given. The result was analyzed. It shows that the EHR in most hospitals need improving and the assessment on EHR adoption can guide the hospital to build their EHR systems better.
Keynote Speaker: Liu, Haiyi, vice chair of CHIMA (China Hospital Information Management Association), and Chair of HL7 China
HAIYI LIU, senior engineer, received the master’s degree in automatic control from Tsinghua University, China in 1985. He is currently the vice chair of CHIMA (China Hospital Information Management Association) and the chair of HL7 China. His research interests are hospital information system, medical image processing and standardization on health information. He served as the director of IT service of Beijing Tsinghua Changgung Hospital (2012—2020), the director of information center of Peking Union Medical School Hospital (2006—2012), the director of computer department of the Chinese People’s Liberation Army General Hospital（1991—2006). Watch his presentation here:
S1: Mobile health assessment and contact tracing apps – Digital Health Technology in Emergency Medicine
21.10.2020 9:00-11:30 Brussels/ 15:00-17:30 Beijing
S1.1 Co-management of COVID-19 in the NHS Scotland
Chaloner will walk through the DHI’s contribution to the Scottish Test & Protect system – used nationally to manage Covid-19. He will describe the how an open, platform based infrastructure was used to create an integrated suite of products for everything from clinical assessment, through to testing and contact tracing. He will touch on method and lessons learnt for future developments.
Speaker: Chaloner Chute, Chief Technology Officer, Digital Health and Care Institute, NHS Scotland
Chal leads on our technical strategy and is responsible for the way we support and deliver technical innovation, by applying systems thinking and methodologies in support of our DHI innovation model. He is devoted to the idea that citizens can be empowered to take an active role in their own wellbeing. Chal believes digital health offers tools to achieve this, and the DHI has the fresh perspective necessary to reconceive of the relationship between the citizen and those who might care for them. He brings a range of skills including a Master’s in Healthcare Management & Leadership and a Master’s in Public Health Policy: Health Systems. Five years working in digital innovation, 4 years working in Scottish Government public policy and has had project, programme, and portfolio management training and experience within the NHS. His specialism is in Clinical Decision Support, Health & care technology and unscheduled care. Watch his presentation here.
S1.2 Role of Emergency medical Systems in Syndromic Surveillance
During this Covid-19 pandemic, we have seen how relevant is on-time information of the affected patients to make the right decisions. Public health services surveillance systems are based on the reporting of diagnostic cases, and this can generate a delay. Diagnosis sometimes requires a test that delays the reporting, or the disease is in the preliminary stages were tests are not so useful… Syndromic surveillance is based on symptoms and historical information to detect anomalies in the daily emergency activity; reports are on real time and represent the actual situation of the health system. Main complains can be used as descriptors to classify the patients in generic groups of cases; this methodology has successfully been used during the flu epidemics. A revision of the pre-covid Syndromic surveillance based on emergency departments and the opportunities of this methodology during this pandemic is the objective of this presentation.
Luis Garcia-Castrillo Riesgo, Past President, European Society of Emergency medicine
Born 1951 (Spain), trained Intensivist as medical specialty, Doctor in Medicine, MD, PhD, Associated Professor, Cantabria University. President EUSEM (2018-2020). Watch his presentation here.
S1.3 The trial of smart medicine in emergency department during COVID-19
COVID-19 is an acute respiratory disease caused by the new human coronavirus (SARS-CoV-2). In the begining of the pandemic, the emergency department was overcrowded, resulting in insufficient medical resources for a certain period of time. We tried to use The following ways to solve the problem, try to achieve the remote pre-examination and triage, the early screening of critically ill patients, data collection and management:
- A machine learning-based model for survival prediction in COVID patients
- Monitoring and Management of Home-Quarantined Patients With COVID-19 Using a WeChat-Based Telemedicine System
- COVID-19 data collection and the management
Professor Wei Jie, Director of Teaching and Research, Department of Emergency and Critic Care Unite, Renmin Hospital of Wuhan University, China
Prof. Jie Wei, MD is Director of Teaching and Researching Section of Emergency and Critical Care medicine, Renmin Hospital of Wuhan university, P.R. China. She is also Vice president of China International Exchange and Promotion Association for Medical and Health care of Emergency Medicine. P.R. China. A member of Standing Committee of China College of Emergency Physicians (CCEP), she is honorary Chairman of Hubei Province Emergency Medical Association, P.R. China. Her research is focused on the Cardiopulmonary cerebral resuscitation and post-resuscitation syndrome, Acute poisoning and the Management of Emergency clinical system. She has published more than 20 papers in reputed journals and has been serving as an editorial board member of Chinese Journal of Emergency Medicine journals (China), Journal of Clinical Emergency Medicine(China) and Journal of Internal Medicine Critical Care(China),etc. Watch her presentation here:
S1.4. Construction of an intelligent multi-point trigger and early warning system for emerging and unexpected infectious diseases based on big data of emergency medicine
The COVID-19 pandemic has highlighted the loopholes in the prevention and control systems of infectious diseases in various countries, especially for emerging and unexpected infectious diseases. How to use the intelligent multi-point trigger mechanism to prevent and control emerging and unexpected infectious diseases has become a public issue of great concern to governments and people in various countries. Actively capture big data from emergency department and social institutions, and automatically and real-time mining, analysis, monitoring, and early warning of big data, forming a sentinel system based on comprehensive research and judgment of data, so as to realize ultra-earl warning, automatic triggering and reporting functions , will be one of the hotspots of medicine in future , especially emergency medicine.
Professor Lyu Chuanzhu, Chairman of Chinese Society of Emergency Medicine, P.R. China
- Professor, Chief Physician, Doctor’s supervisor
- Chairman of Chinese Society of Emergency Medicine
- Director of Key Laboratory of Emergency and Trauma of Ministry of Education
- Director of Research Unit of Island Emergency Medicine, Chinese Academy of Medical Sciences
- Secretary of the Party Committee of Hainan Medical University
- Vice President of Emergency Physicians Branch of Chinese Medical Doctor Association
- Watch his presentation here:
S1.4 Errors in medicine: Technology as support for the doctor of the future?
Since the landmark report “to err is human” in 1999, human errors have become more and more the focus of attention. Misdiagnoses contribute to a large extent to these errors. One possible solution to this problem of “human errors” could be the use of technical aids for diagnoses and triage in emergency settings where resources are limited and quick decisions must be made often on a basis of uncomplete facts. In reality, digital decision aids and triage tools are being used more and more widely by doctors and patients. But what about the evidence of these tools? Can these tools contribute to the fight against the COVID-19 pandemic? Can these technical aids support the doctor of the future or will they even replace him?
Thomas Sauter, University of Bern, and European Society of Emergency Medicine
Thomas Sauter is a trained emergency physician, medical educator with a master’s degree in medical education and initiator and chair of the EUSEM working group “Digital Emergency Medicine”. He was recently appointed to the endowed professorship in emergency telemedicine at the University of Bern, Switzerland. His research interests are digital decision support and triage as well as virtual reality in application in emergency medicine and education. Watch his presentation here:
S1.5. COVID-19 Intelligent Prevention and Control System
Covid-19 pandemic is a disaster for the human being. It is not only the responsibility of doctors and hospitals for fighting against Covid-19. Every resident and community should participate in. In addition, it needs not only the treatment of the disease, but also the prevention, screening, diagnosis, treatment, rehabilitation to achieve effective prevention and control of Covid-19. This lecture will describe part of the work done by Tsinghua University in fighting against Covid-19.
Speaker: Bin Yang, deputy director of Institute for Internet Behavior, director of Research Center for Smart Healthcare, Tsinghua University (China)
Dr. Bin Yang is deputy director of Institute for Internet Behavior, director of Research Center for Smart Healthcare, Tsinghua University. And he is also executive director of China Research Hospital Association, vice president of Internet Hospital Branch, a fellow of Pingan AI Medical Research Center and director of equipment branch of China Medical Rescue Association. Dr. Yang’s research interest lies in the establishment of network order, trust in cyberspace, and network architecture based on universal service, such as telemedicine and community service. He has participated in the planning and implementation of many national projects in the fields of industrial Internet, smart community, smart healthcare, smart government, network governance, and network security. A large number of application results have been formed in the fields of health monitoring management, telemedicine rescue and intelligent medical services. Now he is responsible for the construction of Tsinghua Intelligent Healthcare Framework. Watch his presentation here:
S1.C1. Collaboration ePoster Pitch: Pandemic Preparedness: AI-driven Syndromic Surveillance of ILI using EMS data: A research proposal
Respiratory virus surveillance relies on sentinel reporting and laboratory-confirmed cases, requiring adequate recognition of clinical manifestations, testing and sufficient reporting. This harbours serious weaknesses, especially in case of emerging diseases. Further, traditional surveillance takes time, arguably the largest threat to containment.
All recent pandemics were characterised by respiratory infections and early warning of ILI outbreaks is of paramount importance for future public health emergencies. Syndromic surveillance of ILI in pre-hospital emergency medical service (EMS) data has been reported as timelier than monitoring hospital and laboratory data. Therefore, AI-driven syndromic surveillance of ILI using emergency medical dispatch call (EMDC) data and non-emergency medical call (NEMC) data is a unique, timely and cost-effective early warning solution augmenting conventional surveillance. Our research partner Corti developed a functional AI system for emergency medical services. Remarkably, they successfully tested an AI to detect patients at high risk of COVID-19 based on audio patient interviews. Our research aims to develop this technology into a syndromic surveillance tool to identify ILI outbreaks using EMDC and NEMC data.
Approach: Our proposed surveillance system combines the strengths of AI, geographic information systems, EMDC data and NEMC data into the singular opportunity to recognise EMS demand patterns and unexpected deviations thereof in near real-time. It localises outbreaks across administrative borders using spatial analysis and smoothly communicates alerts to public health authorities.
Cooperation with China: With NIDRIS and CIDAR, China has developed one of the most sophisticated early warning systems globally. Under the Sino-Dutch MoU Health Cooperation China CDC, Beijing CDC, the Chinese Academy of Sciences, RIVM and Maastricht University have a longstanding, flourishing collaboration. Learning from the Chinese surveillance experience would greatly enhance our proposal. We are therefore proposing to initiate a research project involving EU and Chinese EMS centres.
Role of Beijing Center for Disease Prevention and Control, P.R. China: Following the outbreak of severe acute respiratory syndrome (SARS) in 2003, a series of infectious disease surveillance systems including national notifiable infectious disease reporting system, enhanced infectious disease surveillance system, vector surveillance, laboratory-based surveillance and syndromic surveillance were launched in China. These systems have potential to provide timely analyses and early detections of outbreaks. Identifying early, accurate, and reliable signals of health anomalies and disease outbreaks are the main objective of public health surveillance. However, there are two main technical challenges: the data sourcing challenge and the analysis challenge. Passive surveillance system relies on accumulated cases, data which are often delayed and sometimes incomplete; thus, opportunities to contain the spread of diseases are often missed. In addition, traditional early warning models have challenges to trigger timely and accurate abnormal signals. In recent decades, artificial intelligence (AI) technologies, especially deep learning, have been widely applied to infectious disease outbreak detection and early warning, trend prediction, and public health response assessment. They have made positive impacts on timeliness, sensitivity, accuracy, and cost-effectiveness of early warning systems, augmenting traditional surveillance. Surveillance in this field has been enhanced significantly from these recent AI advances. Thus, we believe AI-driven surveillance will mitigate challenges both from the origin of data sources and analytics. Early warning surveillance systems for influenza in Beijing consist of influenza-like illness (ILI) cases surveillance and virological surveillance. Recently, AI has been exploratorily applied in the early warning analysis of influenza and hand-foot-and-mouth disease. However, it has not been used in the data collection. We had collaborated with Maastricht University in the field of early-warning and disease burden estimation of influenza for several years. We would like to build a deeper collaboration in AI-driven surveillance for influenza.
Role of the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (IGSNRR), P.R. China. The pandemic of COVID 19 sweeping the world brings unprecedented loss, reminding us to collaborate strongly particularly in public health (ie. infectious disease control). The Geo-technique (ie. GIS and spatial modelling) functions as powerful method in terms of epidemiological investigation, outbreak prediction, resources allocation and visualization during the COVID 19 in China comparing with SARS in 2003, and provides very helpful guidance for authorities to control the disease in China. As a powerful technique that can integrating multi-source heterogeneous data, geo-technique can be more useful for disease early warning, prevention and control, in particular combining with AI technologies. However, in China and in many countries in the world, due to the lack of data sharing from multi-sources (ie, health department, transportation) and the lack of suitable modellings, the timeliness and the accuracy for disease surveillance are still low. Combining geo-technique and AI, integrating multi-source data, the timeliness and the accuracy for Influenza surveillance can be largely improved. IGSNRR has The National Key Lab of Resource and Environment Information System, with deep experience in geo-modelling and multi data (incl. big data) integration, and has worked years with health authorities on disease prevention and control. We believe we can provide technique support for the project, and definitely, the result of the project can be tested and furtherly used in China for influenza surveillance. Based on our previous collaboration and good track records, we would like to cooperate deeply with Maastricht University in combining Geo-technique and AI for influenza surveillance.
Thomas Krafft, Associate Professor, CAPHRI, Maastricht University, the Netherlands. Trained as a health geographer Thomas Krafft is an Associate Professor at Maastricht University’s Care and Public Health Research Institute (CAPHRI). Further, he is Honorary Professor of the Institute for Geography and Natural Resources Research at the Chinese Academy of Sciences. His research focus is on public health surveillance, urban (environmental) health and global health. Currently he is co-chair of the European Academic Global Health Alliance (EAGHA) and member of the steering group of the World Federation of Academic Institutions for Global Health (WFAIGH). He has been an active member of the MoU Sino-Dutch Health Cooperation
Eva Pilot, Docent, CAPHRI, Maastricht University, The Netherlands. Eva Pilot is a docent at CAPHRI, Maastricht University. She has been involved in scientific projects in the field of health geography, urban health, health equity, health information ecosystems, environment and health and public health surveillance. Her regional focus is on China, India and Europe. She is co-chairing the health geography researcher network. Active member of the MoU Sino-Dutch Health Cooperation.
Simone Doreleijers, Researcher CAPHRI, Maastricht University. Simone Doreleijers is a researcher at CAPHRI, Maastricht University. Trained in Biomedical Sciences and Global Health, her work focuses on urban health, emergency medical services, health innovations and public health surveillance.
Lars Maaløe, CTO and co-founder, Corti. Corti’s CTO and co-founder, Lars Maaløe, holds a MS and PhD in Machine Learning from the Technical University of Denmark. He was awarded PhD of the year by the department for Applied Mathematics and Computer Science and has published at top machine learning venues: ICML, NeurIPS etc. His primary research domain is in semi-supervised and unsupervised machine learning.
Lu Chen, Business Analyst Corti. Lu Chen is a business analyst at Corti and has a Master in Management specialising in Corporate Finance and Data Analysis. Having previously worked in an investment bank and strategic investment department, she has a strong interest in AI and healthcare and rich international cooperation experience. Now she is responsible for business development activities and funding projects.
Helle Collatz Christensen, MD Senior Research, Regional Clinical Quality Development Programme in the Capital Region of Copenhagen. Helle Collatz Christensen is a senior researcher and chief physician at the Regional Clinical Quality Development Programme in the Capital Region of Copenhagen. She has previously been involved in joint research projects with Corti, investigating AI-driven decision support for emergency dispatch in relation to cardiac arrest.
Mette Wenøe. Mette Wenøe is a senior research consultant/administrator. She has a Master in sociology and is skilled in project management and coordination, EU-projects, politics, logistics, finance and administration.
Wang Quanyi, director of the Institute for Infectious Disease and Endemic Disease Control, Beijing CDC. Prof. Wang Quanyi is the director of the Institute for Infectious Disease and Endemic Disease Control, Beijing CDC. He has been working in Beijing CDC since 2002, focusing on early warning, disease burden estimation, and effectiveness evaluation of vaccine and prevention measures of various kinds of infectious disease. Further, he has been an active member of the MoU Sino-Dutch Health Cooperation.
Professor Yang Linsheng, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. Prof. Dr. Yang Linsheng is the director of the Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. He has been working on environmental health for more than 30 years, specific on climate change and health, environmental pollution and health risk assessment and medical geography.
Liselotte van Asten, Epidemiologist, the Dutch Institute for Public Health and the Environment. Liselotte van Asten, Ph.D, has been working as an epidemiologist at RIVM, the Dutch Institute for Public Health and the Environment, since 2004. She is affiliated with the Centre for Infectious Disease Control and her work is focused on influenza and public health surveillance. She has been an active member of the continuing MoU Sino-Dutch Health Cooperation.
S1.C2. ePoster: Patient representation from structured Electronic Medical Records based on Skip-gram algorithm
The secondary utilization of the structured electronic medical record (sEMR) data has become a challenge due to the diversity, sparsity, and high-dimensionality of the data representation. We aimed to explore the feasibility of the embedding-based feature and patient representation for sEMR data and demonstrate the embedding-based patient representation’s efficiency and superiority. Structured electronic medical data with104752 records consisting of 13757 feature concepts were represented in different data representation methods, including the embedding -based representation, the multi-hot representation, and the multi-hot codes combined with initial values. Data in different representations were performed in the patient clustering task, assessed by various performance metrics. Results showed that the feature-level embeddings could reflect the potential associations among medical concepts to some degree. Patient-level embeddings can be easily used as continuous input to standard machine learning algorithms and improve performance. We expect that the embedding-based representation will help a wide range of the secondary use of the sEMR data.
Huang Yanqun, a Ph.D. student at the biomedical engineering school of Capital Medical University in China.
Huang Yanqun, a Ph.D. student at the biomedical engineering school of Capital Medical University in China. Her research field is about medical data mining, statistics, and the secondary use of EMRs data, especially deep learning-based representation learning for patients with structured features based on Electronic Medical Records. Watch her presentation here:
Keynote 2: A patient and engineering perspective on digitalization, data and AI for strengthening health
21.10.2020 – 12:00-12:30 Brussels/ 18:00-18:30 Beijing
For citizens, one of the most important things is their health and their loved ones’ health. Efficient and adequate health care is essential to reach this goal, but it is far from sufficient. Studies show that healthcare is only a minor part of creating health. In this speech, aspects of self-care will be highlighted, focusing on digitalization, artificial intelligence, and informatics. Self-care is today an essential part of the care, from everything to understanding symptoms, to taking the actual medication. During self-care and activities, a person collects a lot of health data. The speech will discuss a systematic review of this kind of data and present what health data is for patients. Knowledge and wisdom are built upon information and data, how we define data and its source will change what kind of knowledge and wisdom we can build; therefore, the definition of health data is important in this context. For people living with long-term conditions, it is vital to living a good life in the presence of illness. This implies a need for good self-care with digital tools and informatics in the area. I Will give a new perspective on this subject and suggest how patients’ work can be eased without losing autonomy or increasing healthcare costs.
Keynote Speaker: Hanna Svensson, Engineer and Patient Advocate, Sweden
Hanna Svensson is a data scientist and engineer in Applied physics, Swedish e-patient, and lecturer on self-care and health data, always with the patient perspective. For 31 years, Hanna has lived with type 1 diabetes and for the last four years with rheumatoid arthritis. Both autoimmune diseases, but with very different kind of self-care. Hanna has worked in a broad field within electronic and software development. She has developed signal processing devices for wireless transmission. She was one of the developers behind the Volvos self-parking concept-car and later developed a different kind of active safety functions. Today, she works at Rise, Research institute of Sweden, with the development of methods for measuring soft variables such as well-being. Watch her presentation here:
S2: Digital health for nursing and rehabilitation – Citizen Engagement with Digital Health Technology
21.10.2020, 12:30-15:00 Brussels / 18:30-21:00 Beijing
S2.1 Nursing Now! Digital health service co-creation and co-design
The European Federation of Nurses Associations (EFN) represents 3 million nurses in the EU and about 6 million nurses in Europe. Its mission is to promote and protect nurses and the nursing profession with reference to the EU, by lobbying the European Institutions, like the European Commission, the European Parliament and the Council of Europe. As part of this mission, the EFN is the Europe regional leader of the Nursing Now Campaign – this campaign aims to improve health globally by raising the profile and status of nurses worldwide. For the EFN, one of the objectives included in achieving the campaign’s goals was to foster among EU policymakers and IT developers the co-creation and co-design of new digital health tools with the frontline nursing workforce. “Co-design” means putting both the developer and the end-user (i.e., the nurses) at the same level with the view to come up with solutions that address actual frontline needs and expectations. Co-creation is the process by which co-design is implemented, in which developers and end-users engage in a continuous and bi-directional dialogue on what the formers need and expect, and what the latter can develop and how. In this dialogue of mutual understanding, in which all stakeholders are at the same level, is where the value of co-creation and co-design lies. As they are the only path towards ensuring that nurses at the frontline of home and hospital care have access to new digital health tools that are fit-for-purpose. And by doing so, achieving end-user impact, deployment, and, ultimately, a better quality of care for patients. To bring these goals closer into the reality, the EFN has joined as end-user partner two EU-funded projects named InteropEHRate and Smart4Health. Both of these are putting citizens at the centre by developing a platform/system of EU-wide interoperable Electronic Health Records. The EFN is participating by providing nursing sensitive data and fostering nursing data collection. It is essential to make sure that the outcome of these projects address the nursing needs, to achieve impact at the healthcare frontline.
Speaker: Paul De Raeve, Secretary General European Federation for Nurses
Paul De Raeve has been a registered nurse since 1984. He obtained a master’s degree in nursing science at the Free University of Brussels, a Master degree in Statistics from the Catholic University of Brussels, and a PhD degree from the Kings College University of London. Paul holds an Adjunct Professorship at the John Hopkins University in Baltimore (US) since 2018. On Work experience, Paul was appointed as staff manager at the Free University hospital of Brussels, part-time delegated to the Belgium Ministry of Health and Environment. He was responsible for developing a national comparable data warehouse for nursing, introducing qualitative indicators within the hospital financing system and providing data for the political decision-making process. In 2002, Paul De Raeve was appointed as General Secretary of the European Federation of Nurses Associations (EFN) and in 2015 EFN members asked him to establish and develop the European Nursing Research Foundation (ENRF). EFN EU lobby activities relate to the promotion and protection of nurses and the nursing profession with particular reference to the EU political agenda, especially the European Social Pillar priorities, including the digitalisation of the health and social care ecosystem. Working towards this mission includes ensuring that nursing is central in the development, implementation and evaluation of the European Health and Social Policy in the field of education, workforce, and quality and safety.Nursing informatics in Palliative care for Cancer. Watch his presentation here:
S2.2 AI+lung computer-aided diagnosis, fundamentals and technologies
Currently, the new coronavirus pneumonia epidemic threatens everyone’s life. The application of medical artificial intelligence in the lungs has bought precious time for mankind. At present, the global medical data volume has reached the trillion GB level and is still growing at an extremely high rate. Compared with the human brain, artificial intelligence can process massive amounts of medical data more efficiently, quickly find features and rules, and at the same time combine a huge medical knowledge base to establish an artificial intelligence computer-aided diagnosis system based on medical big data. This report will focus on AI + lungs, introduce the application of medical artificial intelligence in the lungs, including basic theories and technologies such as high-dimensional reconstruction of complex medical big data, feature extraction, etc., and introduce the research hotspots and latest applications in the field of medical artificial intelligence.
Professor Yao Lu, Sun Yat-sen University, Guangzhou
Professor Yao Lu received the bachelor’s degree in mathematics from the University of Science and Technology of China, Hefei, China, in 2000, the master’s degree from the Institute of Mathematics, Chinese Academy of Sciences, and the Ph.D. degree in mathematics from Syracuse University, Syracuse, NY, USA, in 2009. He is currently a Full Professor with the School of Data Science and Computer Science, Sun Yat-sen University, Guangzhou, China. Prior to joining Sun Yat-sen University, he was a Research Investigator with the Department of Radiology, University of Michigan, Ann Arbor, MI, USA. His research interests are in medical imaging, image processing, medical image analysis, and ill-posed problems. His research has been supported in part by the National Science Foundation of China, Department of Education of China, and the Department of Science and Technology of Guangdong Province, China. He has published over 90 journal papers and conference proceeding papers. He has received awards from the Recruitment Program of Global Youth Experts of Chinese Department of Organization and the Hundreds Talents Program of Sun Yat-sen University. Watch his presentation here:
S2.3 Digital health literacy for nursing & rehabilitation
One of the core objectives of the European Union (EU) is to improve the health of European citizens. eHealth has the potential to empower citizens to better manage their health and disease, improve prevention, enable more accurate diagnosis and treatment and facilitate the communication between healthcare professionals and patients. It can also contribute to a more equal access to healthcare while facilitating access to health information. This requires a sufficient level of health literacy for both patients and nurses.The nurses have to be aware of Digital Health literacy and how the digitalization can either impose a barrier or be a facilitator in the provision of care. To understand the digital aspect the nurses also need to have an understanding of the patient’s digital literacy and eHealth Literacy (1). Education plays a significant role in the understanding of health literacy among nurses. In particular, nurses need new competencies they take on new roles and responsibilities related to digital health transformation and re-orientation of healthcare and help patients navigate between allied health professionals. Consequently, over the past decade, universities and colleges worldwide have increasingly had a focus on awareness among nurses of the importance of patients as well as aspects of nursing students and digital competences addressing these aspects as part of the curriculum.(2) The Danish National Steering Group for the national follow-up groups in health education has set five benchmarks for the development of a technology focus on health education. The benchmarks areset on the basis of the significant changes that the technological development and implementation will bring to the health sector in the coming years, and which will change conditions and opportunities for health professionals and citizens. Relevant benchmarks for the development are:
- The healthcare professional uses technology safely and competently in his practice.
- The health professional supports the citizen’s use of technology.
- The healthcare professional adapts to technological changes.
- The healthcare professional is part of technological innovation.
- The healthcare professional reflects ethically and critically on technology acquisition and use. (3)
These topics are to be discussed in my talk.
- European citizens’ digital health literacy https://ec.europa.eu/commfrontoffice/publicopinion/flash/fl_404_en.pdf
- Health literacy, digital literacy and eHealth literacy in Danish nursing students at entry and graduate level: a cross sectional study .Holt, Overgaard, Engel et al. BMC Nursing 2020 https://bmcnurs.biomedcentral.com/articles/10.1186/s12912-020-00418-w
- Benchmarks for health education technology focus in Denmark. The Danish National Steering Group for the national follow-up groups in health education
Keywords: Digital health literacy for nursing, nursing education, digital knowledge areas.
Speaker: Inge Madsen, RN., MI (Master of Healthcare information.) Aarhus, Denmark.
Inge Madsen is a Registered Nurse (1988) and holds a master in Healthcare Informatics at University of Aalborg in 2008. She is currently Associate Professor at VIA Faculty of Health Sciences, Aarhus, Denmark, where Inge teaches at the Bachelor of Nursing programme. She also teaches health informatics at Healthcare Technology Engineering, University of Aarhus, Denmark. Inge is also teaching in Masters programs in China. Inge has participated in several national and international steering and working groups in Nursing and Health Informatics. She has an extensive background in informatics working in numerous roles such as leader of EHR implementation at the University Hospital in Aarhus, CEO of Health informatics and Clinical Quality at Horsens Hospital in Denmark. Inge has published several articles and textbook chapters for health care professionals. She is the former chair of the Danish Nursing Society and a former board member of the Danish Medical Informatics Society. Inge is one of the founders of the Danish Clearing House and Center of Systematic Reviews where she was a professor until November 2015. Watch her presentation here:
S2.4 Construction of Medical Knowledge Graph Based on Medical Knowledge Model
We are going to build a medical ontology based medical knowledge graph to support structed clinical data capture in EHR and knowledge base maintenance by deep learning. Because the traditional disease ontologies are incompatible each other, We try to build the clinical manifestation ontologies based on medical knowledge model to unify the semantics of clinical manifestations. In China, EHR Template has been widely used to produce the semi-structured patient records and Our research target is going to produce the full-structured patient records based on the medical knowledge graph to support the structed clinical data capture, CDSS and data mining. This is a cooperative research projects including Peking University People’s Hospital, Chinese Academy of Traditional Chinese Medicine, Stanford University Medical School and Clement Graduate University.
Speaker: Professor Yusheng He, Former Director of Medical Informatics Center, People’s Hospital, Peking University
Professor Yusheng He, former Director of Medical Informatics Center, People’s Hospital, Peking University and former member of Health Information Standard Committee for Ministry of Public Health, China. He has two knowledge training backgrounds on medicine and computer science in Beijing Medical University and Peking University and studied medical informatics and medical expert system in medical school of Pittsburgh University and Carnegie Mellon University as a visiting scholar in 1987-1988. He was a CIO for 14 years and in 1995, set up the first large scale hospital information system (HIS) in China for People’s Hospital. His research interesting is in HIS, system integration, intelligent system, the standardization of medical informatics, RHIO and NHIN in China. His research projects are the RHIO implementation and the intelligent electronic medical record. From 2013 to 2014, He was a visiting professor at Claremont University in Los Angeles to cooperate in scientific research with the Stanford Center for Biomedical Informatics Research and the China Academy of Traditional Chinese Medicine, engaged in medical ontology study. We are currently being carried out on the study of ontology based electronic medical records templates. Watch his presentation here:
S2.5 CEN-ISO/DTS 82304-2: How labelling health apps can contribute to a healthier global community
There are thousands and thousands of health and wellness apps: to quit with smoking, to help recognise skin cancer, to monitor symptoms, to provide cognitive rehabilitation, to track sporting activities, etc. etc. However it’s hard for consumers, patients, healthcare providers and care insurers to establish which health app fits their requirements and indeed contributes to their health needs and health issue management. CEN-ISO/DTS 82304-2 was commissioned by the European Commission to help address this challenge. The initiative went global in the cooperation with ISO. An international project team with experts from 14 countries spanning 4 continents has developed a health app quality requirements conformity assessment, building upon existing standards, health app assessment frameworks and a Delphi study with 90 international experts and stakeholders from 6 continents. With the inspiration of the EU Energy label, which has been adopted in full or part by 59 countries outside of Europe, including China, and is also maintained by ISO CEN and IEC, a health app quality label was designed. The label was subsequently tested with low health literates for adequate understanding. CEN-ISO/DTS 82304-2 also comprises a Covid tracing apps – , ethics – and use cases annex, was already referenced in the EU’s Covid tracing apps toolbox and tested with 11 Covid symptom apps. This technical specification is expected to be published early 2021, following a formal vote by ISO member bodies this year.
Speaker: Petra Hoogendoorn, Researcher National eHealth Living Lab, Leiden University Medical Center, The Netherlands
Petra Hoogendoorn is an industrial engineer and change manager. She initiated two health apps in oncology and does research at the National eHealth Living Lab (Leiden University Medical Center The Netherlands). She coordinated the development of CEN-ISO/DTS 82304-2 quality requirements conformity assessment and health app quality label, which is to enable consumers, health professionals and insurers in deciding what health app suits their needs, and tested Corona symptom apps with 82304-2. Watch her presentation here:
S2.6 Hospital Nursing Information System in China – Now and Future
Nursing Information System (NIS) is an important part of the whole Hospital Information System (HIS). Demand for more intelligent NIS has been an explosion in China not only because the growth economy, but also because the ongoing development of nursing profession further boosts the demand. With the prosperous of the Internet Era, NIS in China is now playing important roles in many aspects, not limited to clinical nursing practice, but also nursing management, research and education, from outpatient admission to the follow-up management after discharge. Evidence showed that NIS effectively improved the efficiency and accuracy of nursing work. It is of great significance for promoting standardization of nursing management. Moreover, a human-computer interaction intelligent decision support system integrated with electronic nursing records may become the future development trend of NIS in China.
Speaker: Shu Ding, Nursing Supervisor at Beijing Chao-Yang Hospital, Capital Medical University (China)
Shu Ding is a Nursing Supervisor at Beijing Chao-Yang Hospital, Capital Medical University. He earned his bachelor’s and master’s degree in nursing at Capital Medical University, Beijing, China. His doctoral project mainly focuses on testing individually tailored interventions implementing mHealth technology to promote health and well-being of patients with cardiovascular disease, particularly by understanding how mHealth guided health behaviors affect clinical outcomes. Mr Ding’s research interests include cardiovascular nursing, clinical nursing quality control and improvement, especially using information technology and nursing knowledge to benefit both clinical practice and patient safety. Ding helped to improve nursing information system in Beijing Chao-Yang Hospital and co-create a structured electronic nursing record system for venous thromboembolism care. One of his research projects entitled with Visual Intelligent Management of Nursing Safety Based on Data Mining was funded by Capital Medical University. In addition, he served as an invited tranlator for NI2016 International Conference in Geneva Switzerland and 2016 CMIA Conference in China, and a reviewer for the MedInfo 2019, the 17th World Congress on Medical and Health Informatics. He is the Secretary of Male Nurse Committee of Chinese Nursing Association and helped to establish the first national committee. Mr Ding was awarded research grants from Beijing Hospitals Authority Youth Programme, Beijing Municipal Commission of Education, etc. Watch his presentation here:
S2.C1 Collaboration ePoster Pitch: Can we use Patient Summaries to strengthen medical documentation in nursing homes?
The COVID-19 pandemic has illustrated how vulnerable the oldest population is and how poor the clinical data is to inform sound epidemiological responses. In Belgium (a country with 11.5 million inhabitants, 144,000 nursing home beds, almost 10,000 deaths were recorded during the epidemic. Two thirds of them occurred in nursing homes. Hence, mortality rates in the nursing homes were approx. 5,000 deaths per 100,000 residents, compared to approx.. 30 per 100,000 in the rest of the population. The COVID-19 pandemic has illustrated how vulnerable the oldest population is and how poor the clinical data is to inform sound epidemiological responses. We propose to engage the General Practitioners responsible for the care of nursing home residents to ensure minimal medical documentation by filling and maintaining an International ePatientSummary for every resident. Nursing homes should be equipped with a local database to keep and protect these data, while being open to distributed analytics for nation-wide research and computerized decision support. This can support the health professionals in assuring appropriate prescribing, reduction of inappropriate polypharmacy, and quality audit of the clinical data. This approach has been tested in a pilot study in 3 nursing homes in Belgium1, and could be upscaled by bringing in results from other European projects such as C3-Cloud. It could be a format for a practical approach to enhance the quality of medical documentation and prescribing in Nursing Homes, in other countries and in China.
- Wauters M, Elseviers M, Dilles T, et al. OptiMEDs Pilot Study – Full Text View – ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT04142645 (accessed 29 Jun 2020).
ePitch Speaker: Robert Van Der Stichele, Professor Emeritus, University of Ghent, Belgium (Europe)
Professor Robert Van Der Stichele is a practicing family physician in Ghent, Belgium, since 1978. He combines his clinical practice with research projects since 1982 He obtained his PhD (in medical sciences) in 2004, and was appointed as teaching professor in the department of Pharmacology in the University of Ghent, where he is today professor Emeritus. Watch his presentation here:
S2.C2. Collaboration ePoster Pitch: MyData for COVID-19
Modern data legislation increasingly empowers citizens, and therefore patients, with rights to access and control their health data. The mechanisms needed to exercise modern data rights are currently underdeveloped and underserving individuals and societies. MyData is the human-centric approach to shift the power of personal data more equitably into the hands of individuals as part of a fair data economy. In this article, we present different scenarios that apply the MyData principles for human-centric control of health data. These scenarios demonstrate the potential of the human-centric approach for turning data rights into truly actionable points for policy makers, healthcare stakeholders, and medical communicators.
Fredrik Linden, MyData Hub
Fredrik has extensive domestic and international experience in eHealth and the healthcare industry and he has an understanding of its key business drivers. This has given him a diversity of skills in the eHealth field and recognition as a distinguished international leader in a multidisciplinary environment. He has the ability to analyze and synthesize information and does problem-solving as well as to plan and follow through on commitments. He has always been driven by a strong social mission and a desire to improve the efficacy and efficiency of healthcare systems, mainly by improving the eHealth solutions’ ability to connect scientific research and healthcare delivery for mutual benefit. He, therefore, cofounded the Swedish MyData hub as a not-for-profit to further our human and digital rights. Watch his presentation here:
Thursday 22 October 2020, Day #2: Medical Imaging, Robotics and Standards
Keynote 3: Orphanet – Taking the next step in the management of Rare Diseases
22.10.2020, 08:00-08:30 Brussels/ 14:00-14:30 Beijing
Rare diseases (RDs) are numerous (~6,000), heterogeneous in nature, and geographically disparate. Few are preventable or curable, most are chronic and many result in early death. Despite their heterogeneity, RDs share commonalities linked to their rarity that necessitates a comprehensive public health approach. The challenges arising from their low prevalence, have led to RDs emerging as a public health priority in Europe. Indeed, while each disease represents less than 1 out of 2,000 inhabitants, RD prevalence altogether is estimated to be 3.5-6%. Point prevalence is the most appropriate indicator for RDs as it provides a measurement of the population burden of disease, and can thus inform focused service delivery targeted at the specific needs of RD patients, pharmacoeconomic evaluation of orphan drugs, appropriate health and social service commissioning, and facilitation of clinical trials. It is also essential for current orphan drug legislation objectives to stimulate the development of RD treatments by incentivizing to compensate for the small market size. However, RD are underrepresented in currently used coding systems, so hampering data generation for evidence-based policy making and research. Orphanet, the currently most comprehensive knowledge base on RD, produces a specific codification system: Orphanet nomenclature of RD (ORPHAcodes), that is progressively being implemented in EU member states health information systems and in ERNs’ registries. A specific EC-funded project, RD-CODE (www.rd-code.eu), delivers guidelines and tools supporting implementation. Orphanet is at the crossroads of the RD data ecosystem, bridging healthcare and research settings, and delivering a comprehensive, standardized, evidence-based, interoperable, versioned, computable and free nomenclature specific for RD.
Keynote Speaker: Ana Rath, Director of INSERM US-14 Orphanet
Ana Rath is a medical doctor with a background in general surgery and a Masters degree in Philosophy. She oriented her career to medical information and terminologies in 1997 and joined Orphanet (www.orpha.net) in 2005, where she was Manager of the Orphanet Encyclopaedia, then Scientific Director, and Director of Orphanet and Coordinator of the Orphanet network since 2014. Ana was the coordinator of RD-ACTION, the EU Joint Action for rare diseases (2015-2018) and of the IRDiRC’s Scientific secretariat until 2017. She chairs the Orphanet Rare Disease Ontology (ORDO), and was member of the WHO’s ICD11 Revision Steering Committee. She currently coordinates the RD-CODE on implementation of RD codification in EU member states project and co-chairs the EJP RD Pillar 2 on data and resources ecosystem for RD research in Europe. Watch her presentation here:
S3: Findable Accessible Interoperable Reusable (FAIR) Health Data Sets – Health technology standards and interoperability
22.10.2020, 8:30-11:00 Brussels/ 14:30-17:00 Beijing
S3.1 HL7 in China
In this report, the speaker will cover
- Introduction of HL7 and HL7 China
- Data Interoperability and the needs of healthcare information standard
- HL7 China standard development、education and conformance testing
- The current standard development and standard testing work.
Jingdong Li, HL7 China (CN)
Jingdong (JD) Li is a physician, medical informaticist, and software architect with 20 plus years of experience in clinical medicine (general and thoracic surgery) and healthcare IT. He is proficient in clinical terminologies, clinical quality reporting, and health data exchange standards, and the healthcare software development life cycle. JD is a subject matter expert in the Office of the National Coordinator (ONC) Query Health technical workgroup (2014), HL7 Infrastructure and Messaging Technical Committee Co-Chair (2008-2010) and is a member of the National Quality Forum (NQF) eMeasure Feasibility Testing Expert Panel (2014-15). He is HL7 CDA, RIM, V2.5 certified, and is co-editor of multiple Health Level 7 (HL7) interoperability standards. Watch his presentation here
S3.2 HL7 supports large-scale COVID-19 testing in the Netherlands
As of June 1st, all people in the Netherlands with mild symptoms of COVID-19 can get tested. On June 30th the national association of regional health centers announced that 250,000 tests had been administered. For a population of a mere 17 million, that is quite impressive. How did we achieve testing at this unprecedented scale? HL7 plays an essential role. Testing for infectious diseases in the Netherlands is the responsibility of regional centers for public health (GGDs). Across the country we have 25 such organizations, jointly represented by their national association, GGD GHOR Nederland. Under normal circumstances, testing for infectious diseases is not such a big deal. The Centers for Sexual Health, part of the GGDs, report 150,000 visits annually. The number of active cases of tuberculosis has not risen above 1000 for the last couple of years. Suddenly the GGDs were told to prepare for 30,000 tests per day, with a possible increase to 70,000 per day in the fall. This meant opening over 60 drive-thru testing locations, educating personnel to properly conduct the test, and opening a call-center to schedule appointments. The national number was called over 300,000 times on the first day alone. All of these are major achievements in their own right. But where do you find the labs that can actually carry out the analysis at this scale? The required tests are so-called PCR tests, which call for rather advanced equipment. During the early stages of the pandemic, some 60 labs were accredited for SARS-CoV-2 virus detection, using the PCR test. And in order to fill the projected numbers, all these labs were needed to pitch in.
So how do you process that many tests on a daily basis? GGD GHOR Nederland has chosen to develop one national solution for COVID-19 diagnostics, called CoronIT. The rationale behind this decision is that the available testing capacity needs to be allocated to the places where it is most needed. That won’t work when you have to connect existing 25 regional solutions with 60 different labs across the country. Even with one national solution, connecting 60 different labs is already quite a challenge. Fortunately, we have HL7 well established in our labs in the Netherlands. A dedicated first group of so-called pandemic-labs (normally working in other fields, such as veterinary labs or cervical cancer screening) had already established connections to the national CoronIT system, using a highly simplified version of HL7 version 2.5 messaging. Being pandemic-labs, they were only commissioned to run the PCR analysis, and hence did not receive any patient information. In times of crisis, regular labs also assist the regional GGD in epidemiological analysis, so they go well beyond the technical analysis of the swab. They need fully functional clinical information exchange based on the full scope of HL7 version 2.5 lab ordering and results reporting. In the middle of April, a pilot implementation was started to connect a COVID-19 lab to the national CoronIT solution. People at the lab, their LIMS vendor, and the team behind CoronIT worked closely together to make this happen. Luckily they could build upon all the work on lab information exchange that had been done in the past by LIMS vendors, professional lab associations, HL7 Netherlands, IHE Netherlands, and Nictiz (the national competence center for electronic exchange of health and care data). Combined with recent experiences on routine reporting of antibiotics resistance data to the national Center for Infectious Disease Control and Lab2lab communications for national genetic typing of resistant bacteria, a solid community of expertise and trust could be engaged. The common understanding was: we can do this!
Before the pilot was actually in operation, other labs started to join already. Early May, the pilot was operational, and by the end of May the first phase of 20 labs was connected to the national CoronIT system, ready for the big-bang of June 1st. Together with the dedicated group of pandemic-labs the testing capacity was sufficient to serve the needs of the population. Luckily, the numbers didn’t rise to the predicted 30,000 tests per day, but have stabilized around 10,000 per day. However, as the country is lifting more and more of the lock-down measures, preparations for an increase up to the predicted 70,000 tests per day in the fall is ongoing. The next phase consists of another 20 labs that are working hard to get their connection up and running in the course of July. In all, we will have connected 50 of the 60 accredited labs, including the pandemic-labs, within the course of 4 months.
It is still a lot of hard work on all levels, from firewalls and character sets, using OML, ORU, OBR and OBX, by coding LOINC and SNOMED CT, all the way to contracts between regional GGDs and labs and the national funding of COVID-19 diagnostics. But without the dedicated community of expertise around IHE/HL7 lab information exchange in the Netherlands, we would never have been able to pull this off. Good old HL7 version 2.5 has proven to be indispensable in the fight against COVID-19 in the Netherlands, because it has united people around a common purpose. In times of crisis these people will roll up their sleeves and get the job done.
Robert Stegwee, Chair CEN TC251/ European Standards Institute
Robert Stegwee is a consultant for Health Informatics, based in the Netherlands. His passion is in meHealth: improving the healthcare experience from a healthcare consumer and professional perspective. He has been involved in healthcare IT in different capacities since 1993, starting in a hospital environment and consulting in different sectors of healthcare, including at a national and international level. In addition, Robert has held a professorship in eHealth Architectures and Standards at the University of Twente. He is currently an independent consultant with his own consulting firm Trace-Health. Healthcare interoperability standards form a topic that is at the heart of effective multivendor architectures, as appropriate in healthcare. Robert started to participate in the development and implementation of the Health Level 7 standards in 1995 and currently serves as member of the board of HL7 The Netherlands, chair of CEN Technical Committee 251 on Health Informatics, and member of Joint Initiative Council on Global Health Informatics Standardisation. With this collaborative spirit in mind, he has contributed to a number of European projects in this area and continues to do so. Watch his presentation here:
S3.3 Using openEHR to implement the Guideline-based CDSS for Covid-19
Coronavirus disease 2019 (COVID-19) is a global pandemic affecting more than 200 counties. Efficient diagnosis and effective treatment are crucial to combat the disease. Computer interpretable guidelines (CIG) can help the broad adoption of evidence-based diagnosis and treatment knowledge globally. This study aims to develop a shareable CIG for COVID-19 and implement a Guideline-based CDSS using openEHR. The latest Guideline of COVID-19 Diagnosis and Treatment in China was selected as the knowledge source for modeling. Firstly, a shared data model based on the openEHR modeling approach were developed to facilitate the data interoperability among systems. Secondly, openEHR Guideline definition language (GDL) was used to capture the clinical rules. Finally, the guideline-based CDSS were implemented to support the diagnosis in the clinics and screening susceptive cases in hospital.
Xudong Lu, Professor, Medical Informatics in Biomedical Engineering Department of Zhejiang University
Xudong Lu is the Professor of Medical Informatics in Biomedical Engineering Department of Zhejiang University/China since 2012, the Visiting Research Professor of Information Systems Group of Industrial Design Department of Technical University Eindhoven/the Netherlands since 2013. He is also the Secretary General of Chinese Medical Software Association since 2014, the member of American Medical Informatics Association and Hospital Information Management System Society since 2007, and the Management Board Member of openEHR Foundation since 2018. His research interests include clinical data modeling, clinical data integration, health big data analytics, clinical decision support and clinical process intelligence. He has led several national projects since 2006 and published over 100 publications around these areas. Watch his presentation here:
S3.4 Translating FAIR principles to Health Care
Data-driven technologies are shaping the landscape of our daily lives and health. Predictive and prescriptive analytics methods change how health care is delivered, precision and personalized medicine require more and more data, including real-world data generated by patients or collected from the routine care system. Although health care has a long-lasting experience in interoperability, reusing data in another context than it is produced, exposes additional challenges. FAIR principles provide guidelines for improving data reuse, specifically in a machine-actionable way. Implementing these guidelines in the context of health care will lead to many opportunities, including supporting the development, testing, and validation of machine learning and artificial intelligence. The RDA FAIR Data Maturity WG  introduced a model for guiding researchers for FAIR data transformation. Also RDA Reproducible Health Data Services WG is exploring how level of FAIRness of curated data sets in health care institutions can be improved . However translating the FAIR guidelines in the health care context is not straightforward. First, the sensitive nature of personal data hinders data sharing and requires advanced solutions such as analyzing FAIR data in a distributed manner. Second, the difference between metadata and data gets blurred, sharing a meaningful level of metadata for discovering relevant data sets, might expose personal information. In this talk, I will highlight health care specific challenges for FAIR data and explore potential solutions.
Oya Beyan Researcher, Fraunhofer Institute for Applied Information Technolgoy, RWTH Aachen University, Research Data Alliance Germany
Oya Beyan is a researcher at Fraunhofer Institute for Applied Information Technology and at the Department of Computer Science at RWTH Aachen University. Her research focuses on methods of data reusability and FAIR data, data-driven transformation and distributed analytics. Her area of expertise is in the semantic web technologies and application of them in health care and life sciences. She actively contributes to the national and international initiatives to enable the adoption of FAIR principles and develops tools and infrastructures supporting FAIR data. With her interdisciplinary background in informatics, medical informatics and sociology, she developed a focus on societal reflections of data-driven change. Oya is a partner in H2020 IMI FAIRplus project and develops a FAIR Maturity Model to guide organizations to improve their data processes delivering reusable and machine actionable data. Watch his presentation here:
S3.5 FAIR Health Data: A European Perspective
FAIR is a great acronym that everybody uses happily to refer to the applicable principles to research data (and any other kind of data) Findable, Accessible, Interoperable and Reusable. However, to apply these principles and to make Research Data FAIR, implies a lot of technical work, standardization and will by the research performance institutions, the researchers and research supporters. In this talk they are going to be reviewed the FAIR principles from the European perspective, targeting in one hand, one of the mail Open Science challenges in Europe (FAIR data) and in the other hand the eInfra that will make Open and FAIR data to flourish (EOSC, the European Open Science Cloud). We are going to reflect other particular circumstances affecting health data sharing and health data FAIRification, like the current rules (GDPR) or technological tradition.
Dra. Eva M. Méndez Rodríguez, Associate professor. Library and Information Science Department, Universidad Carlos III de Madrid
Ms. Eva Méndez holds a PhD in Library and Information Sciences (LIS) and is an expert in metadata. She is Deputy Vice President for Scientific Policy-Open Science at Universidad Carlos III de Madrid and professor of the LIS department. She is the chair of the EU Open Science Policy Platform (@euospp) and RDA ambassador for Interdisciplinary Research. She defines herself in Twitter (@evamen) as an ‘open knowledge militant’. Watch her presentation here:
S3.6 Open Science Cloud Practice in CSTCloud
Open science frees the door for accelerating scientific discovery, especially in cross-border researches. And to make science open, all research resources, including research data, software, codes, algorithms, documents, models, as well as others within the whole research cycle should be better exchanged and shared. E-infrastructure plays a vitally important role in facilitating the reuse of all these different research resources. Many facilities are developed to support such work, like the European Open Science Cloud, the National Research Infrastructure in the US, the ARDC e-infrastructure in Australia, the African Open Science Platform, and all the others around the world from international, national, regional, and institutional levels. However, facing grand human challenges nowadays, better connectivity and interoperability become urgently needed, particularly for better exchanges of various research resources running on those platforms in large-scale collaborations. The idea of developing a Global Open Science Cloud (GOSC) is proposed and discussed during the CODATA 2019 Beijing Conference. Later, representatives are joined together for the further co-design of GOSC, such as the collaboration between CNIC and CODATA on policy study  and the cloud federation testbed under construction between CNIC and EGI in Europe, etc. Here, in this talk, we would briefly present the trends of open science and open science cloud at first. Then CSTCloud ( Chinese Science & Technology Cloud ) and her progress in federation cloud testbed are introduced. Later, biological demonstration based on the cloud federation testbed is analyzed. Finally, an open discussion is followed for the future development of GOSC with the health aspect involved also.
 See more in CODATA FAIR Convergence meeting 2020 Global Open Science Cloud Session. Available at: https://conference.codata.org/FAIRconvergence2020/
 See more in EGI 2020 conference Global Open Science Cloud Workshop. Available at: https://indico.egi.eu/event/5255/
Lili Zhang, Researcher, Computer Network Information Center of Chinese Academy of Sciences
Zhang Lili is a research scientist at the Computer Network Information Center of Chinese Academy of Sciences. Zhang received her Ph.D. degree in information management from Peking University, China and she’s a member of CODATA International Data Policy Committee. Her research focuses on open data and open science policy, practice; information economics. She had been serving as the deputy director of editorial office of China Scientific Data (www.csdata.org) from 2015 to 2018. And currently, Zhang is involved in cloud federation governance and policy study for CSTCloud ( Chinese Science & Technology Cloud ) and her strategic partners around the world. Watch her presentation here:
S3.C1 Collaboration ePoster Pitch: Tools for the support of data workflows in Health Research to make them Findable, Accessible, Interoperable and Reusable
FAIR4Health, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824666, promotes the application of FAIR principles in data derived from publicly funded health research initiatives to share and reuse them in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR in Health. FAIR4Health has designed a workflow to apply the FAIR Principles to health research data, based on the FAIRification process of GO FAIR, but addressing the ethical, legal and technical aspects that health data includes due to its nature. These aspects have been analysed for sensitive data and FAIRification tools, based on the use of the HL7 FHIR standard, have been developed to obtain FAIR data from data resulting from biomedical research. To apply the FAIRification workflow designed in FAIR4Health, data-curation-tool and data-privacy-tool have been developed and are in the testing phase. Likewise, graphical user interfaces of the FAIR4Health platform have been developed and the latest technical developments are being completed. Subsequently, the FAIR4Health platform will be validated with the two pathfinder case studies that have already been designed: 1) Identification of multimorbidity patterns and polypharmacy correlation on the risk of mortality in elderly; and 2) Early prediction service for 30-days readmission risk in COPD patients. Cooperation opportunities with China will be addressed through two lines: a) RDA WG on ‘Raising FAIRness in health data and health research performing organisations’ that has been recently endorsed, aiming to define and implement global guidelines for Health Research Performing Organizations to implement a FAIR data policy; and b) FAIR4Health open community, whose membership is being finalized and will be published on the FAIR4Health website.
ePitch Speaker: Celia Alvarez, MSc, Andalussian Health Service
Health Engineer, MSc in Information and Communications Technologies Management at the University of Seville. Researcher on Research and Innovation Group in Biomedical Informatics, Biomedical Engineering and Health Economics at the Virgen del Rocío University Hospital – Institute of Biomedicine of Seville (Spain). Five years of experience as a researcher in medical informatics projects at regional, national and European level. Reviewer of scientific papers in Journal of Medical Internet Research (JMIR Publications) and Methods of Information in Medicine. Member of Research Data Alliance (RDA) and chair of Raising FAIRness in health data and health research performing organisations (HRPOs) RDA WG. Member of several RDA groups: Health Data Interest Group, Reproducible Health Data Services WG, FAIR Data Maturity Model WG, Research Data Repository Interoperability WG and Data Versioning. WG. https://orcid.org/0000-0001-8647-9515 .
S3.C2. Nordic Health Data Collaboration
The Nordic countries hold a unique position to become a world-leading health innovation hub for the benefit of patients and society with high-quality health data covering more than 25 million people. Since 2019 the Nordic Health Data Collaboration have worked to create awareness of Nordic health data strengths and kick-start collaboration across the Nordics. In Denmark, the Data Saves Lives partnership has presented specific and value-creating solutions that improve overview and access to Danish health data since 2017. The solutions have been enrolled in the national health data strategy and are now being implemented alongside other great initiatives to unlock the potential all over the Nordics. Join the movement – learn about our partnership and discuss how we can improve overview and access to health data across the Nordic borders and internationally. Together, we can overcome the challenges by learning from each other. We are always looking for great cases and collaborations showing a FAIR, safe and innovative use of health data globally!
ePitch Speaker: Louise Buch Rosenlund, Head of Intl. Development and Partnerships, Data Saves Lives / CPH Healthtech Cluster
Louise is senior development manager with 4 years of experience with public-private innovation projects, international development and marketing. In charge of Nordic and international activities in the health data initiative Data Saves Lives in Denmark since 2019 with a focus on engaging with key partners globally to build an ecosystem and identify new technologies and datadriven solutions for better use of health data. The aim is to position Danish and Nordic health data strengths internationally and implement new initiatives in collaboration with a broad range of Danish and international companies, health operators and researchers.
Keynote 4: Innovation of 3D Medical Imaging, Processing and Visualization for Intelligent Minimally Invasive Diagnosis and Therapy
22.10.2020, 12:00-12:30 Brussels/ 18:00-18:30 Beijing
Medical imaging, processing and visualization play a fast-growing role in high precision minimal invasive diagnosis and therapy. Medical images are expected to present an intuitive and accurate real-time guidance for surgeons or therapeutics systems, making it efficient to reduce invasiveness in surgical treatment. In the field of intra-operative imaging and processing, integrated diagnosis and therapeutic systems, which combines preoperative and intraoperative images have been established for precision tissue identification, tumor resection during surgery. The accuracy of the intraoperative detection of the tumor is improved by using high-precision dynamic optical analysis. We study novel image processing and multimodality image fusion methods in the field of quantitative and automatic analysis of lesions and anatomic structures to guide accurate diagnosis, efficient implant determination and radiation-free intraoperative soft catheter navigation. The separation issue between guidance information and surgical area may cause surgeon’s hand-eye discoordination problem. We develop a naked-eye three-dimensional (3D) medical image visualization method called integral videography with a full parallax and high geometrical accuracy. The novel 3D medical display method has performed significant advantages in augmented reality image guided surgery. We further design a real 3D see-through surgical navigation system that enables surgeons to see the images of internal structures merged in the surgical scene. The systems have been evaluated in the area of neurosurgery, orthopedic, and dental implantation. The future works include the better integration of multi-module diagnosis and therapeutic techniques under the guidance provided by high-precision and intuitive medical images.
Keynote Speaker: Professor Hongen Liao, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
Dr. Hongen Liao is Professor and Vice Chairman of the Department of Biomedical Engineering, School of Medicine, Tsinghua University, China. He received his Ph.D. degrees in biomedical precision engineering from the University of Tokyo, Tokyo, Japan in 2003. He was a Research Fellow of Japan Society for the Promotion of Science (JSPS). From 2004, he was a faculty member at the Graduate School of Engineering, The University of Tokyo, where he became an Associate Professor in 2007. He has been selected as National “Thousand Talents” Distinguished Professor, National Recruitment Program of Global Experts, China since 2010. He is Director and Founder of the Advanced Theranostics and 3D Imaging Laboratory, Tsinghua University. Professor Liao’s research interests include 3D medical image, image-guided surgery, medical robotics, computer-assisted surgery, and fusion of these innovative healthcare technologies for minimally invasive precision diagnosis and therapy. He has also been involved in long viewing distance autostereoscopic display and 3D visualization. He is the author and co-author of more than 250 peer-reviewed articles and proceedings papers, as well as over 50 patents, 290 abstracts and numerous invited lectures. Dr. Liao was distinguished by receiving multiple government awards and various Best Paper Awards from different academic societies. Prof. Liao is an Associate Editor of IEEE Engineering in Medicine and Biology Society Conference, the General Chair, Program Chair and Organization Chair of multiple international conferences including MIAR, MICCAI, and ACCAS. He has served as a President of Asian Society for Computer Aided Surgery and Co-chair of Asian-Pacific Activities Working Group, International Federation for Medical and Biological Engineering (IFMBE). Watch his presentation here:
S4: Medical Imaging and Robotics of health and social care
22.10.2020, 12:30-15:00 Brussels/ 18:30-21:00 Beijing
S4.1 Accident and Emergency Informatics (A&EI) to Establish Inter-Machine Communication in the Early Rescue Chain.
Accident & emergency informatics (A&EI) is the trans-disciplinary science of systematically collecting and managing environmental, behavioral, physiological and psychological data in order to forecast, prevent, or lower the impact of such events on the subject. Smart homes, vehicles, or clothes can be turned into diagnostic spaces recording A&EI data. Furthermore, the rapid dissemination of smart devices bears the potential of automatic emergency alerts, which are transmitted between machines without any human in the loop. However, there is not yet any interconnection between the – so far – stand-alone information and communication technology (ICT) systems involved in accidents and emergencies, namely alerting systems (e.g., smart home/vehicle/wearable), responding systems (e.g., ambulance), and curing systems (e.g., hospital). We define the International Standard Accident Number (ISAN) as a unique token for interconnecting the ICT systems. Based on business analytics in the emergency care, we derive technological, syntactical, and semantical requirements for the ISAN. We propose a compact alphanumeric representation that is generated easily but worldwide uniquely by the alerting system. Furthermore, an ISAN embeds time and position of the event and an identifier of the alerting system) As an example, we show how the ISAN is used by a smart home to establish machine-to-machine communication. The smart home detects a fall of an elderly person that lives alone, creates the ISAN, and transmit it with the alert to the responding system, which in turn uses the ISAN to get access to the smart home’s internal data: medical records of the affected subject, floorplan indicating event and fastest way to the event location, etc. Also, ISAN is used to inform the smart home on arrival of the rescue team, and to transfer the fingerprint of the emergency physician such than she can open the door immediately.
Thomas M. Deserno, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Brunswick, Germany
Prof. Dr. Thomas M. Deserno (born as Lehmann) studied electrical engineering and medical informatics at RWTH Aachen, Germany, before becoming the CEO of Campus Braunschweig at the Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School. His research interests include medical image processing applied to quantitative measurements for computer-assisted diagnoses, medical research in controlled clinical trials, as well as seamless workflow integration of image and signal analysis, in particular for applications in accident & emergency informatics. He is Senior of IEEE and Fellow of SPIE, and associated editor of many journals such as PLOS ONE, European Journal for Biomedical Informatics, Methods of Information in Medicine, or SPIE Medical Imaging. Furthermore, Dr. Deserno is the German representative in the International Medical Informatics Association (IMIA). Watch his presentation here:
S4.2 Robotics Assisted in Spine Surgery: Practice and Trends
Robotic assisted surgery techniques, which have the potential to extend surgeon’s physical capabilities with advanced image guidance system, have been used step by step in spinal surgery in recent years. This study focus on the development and application of robot techniques in spine surgery in our hospital. It starts with an overview of the definition and development history of various robots, including the first medical robot which based on the industrial platform designed for stereotactic brain surgery. The advantages and disadvantages of typical orthopaedic robots such as Acrobot precision surgical system, the RIO robotic arm interactive orthopaedic system and SpineAssist were also discussed. In robotic assisted spine technology, there were some technical difficulties related to safety issues, such as how to minimize the system error of the robotic system and how to present a virtual relevant anatomy to the doctors, and some difficulties related to surgical procedures, such as how to integrate these techniques into the already exist procedures. For the questions which orthopaedic robots were facing, we shared our own experiences in design and current applications of orthopaedic robotic system. In our strategies, we firstly improved navigation accuracy based on 3D images, then integrated the navigation system and robotic arm into a more complex orthopaedic robotic system. This orthopaedic robotic system was approved by CFDA with independent intellectual property rights. This robot system includes a 6-degree of freedom (DOF) robotic arm and a real-time navigation system, the clinical error was smaller than 1.0 mm. We have done multiple random study both on lumbar spine and cervical spine , which shows the accuracy of robot assisted spine pedicle implantation is much better than free hand method. The application for the robot were not only limited in spinal surgery but also can be used in the traumatic surgery. Several cases assisted by this robotic system were shared. The challenges and research areas for the future progress in this field were also discussed.
Professor Da He, Spine surgery department, Beijing Jishuitan Hospital, Peking University.
Dr. Da He is an associate professor of spine surgery department, Beijing Jishuitan Hospital, the 4th clinical college of Peking University. Beijing Jishuitan Hospital is nationally renowned for its achievement and attainment in orthopaedics, and it ranks at 1st place for over 10 years in China, and spine surgery department is an acclaimed component of Jishuitan. Dr. Da HE receives over 6000 outpatients and completes 500 cases of spine surgery every year. His research interests include navigation and robot assisted spine surgery, none fusion technology in spine. Da HE is a committee member and secretary of spine group in Chinese Orthopaedic Association, the 10th Vice President of Youth Committee in Chinese Orthopaedic Association, Executive Member of Asia-Pacific Cervical Spine Society. Watch his presentation here:
S4.3 When your best co-worker is a Robot? I Have one!
At the hospitals, much of the workload consists of administrative and repetitive tasks. This is mostly done by clinical staff and takes time from the patient’s actual meeting and doesn’t need any medical expertise. Some of the repetitive tasks done by administrative staff, but don’t require any expertise, are time-consuming, repetitive, and boring. How about employing an administrative robot instead? Södra Älvsborgs Sjukhus (SÄS) has done precisely that. Today SÄS has five robots that work around the clock to support repetitive administrative work. Besides, they also have five backup robots that assist when called on, at any time during the hour. Our robotic co-Workers have reduced the time needed in these processes by thousands of hours per year. Created a faster flow of information, reduced waiting time for the patients, and increased the quality (reduced the number of errors in the administrative systems, thereby improving the quality in the meeting between patient and care staff). During the first years, it was a slow process to create robots due to the innovative way of using them. Considerations were taken to legislation questions as well as adapting to the existing IT-environment. Today the time needed to automate a process using RPA is a matter of weeks. They were especially proven to be useful when the Covid-19 pandemic increased pressure on the hospital and our staff. We could use our previous knowledge to quickly use more robots to help us out in the Pandemic situation. That saved meaningful time for staff that could better use this time for patient support.
Fredrik Hansson, development leader at Södra Älvsborgs Sjukhus (SÄS)
Fredrik Hansson is a development leader at Södra Älvsborgs Sjukhus (SÄS). He has a Msc in mechanical engineering. His primary role as a development leader at the hospital is to assist in the overall development of the hospital and participate and achieve changes in specific areas or teams at the hospital. For the last three years Fredrik has focused on developing innovative solutions at SÄS. This include Robotic Automation Process (RPA) and AI. He has both worked to create the existing solutions but also with the implementation of the robots in daily practice. This includes change management, day-to-day support, and present guidelines for working with a robot in your team. Watch his presentation here:
S4.4 Applications of Robotics and AI in Surgery
The orthopedic industry has an extremely broad market, with up to 20 million cases of bone trauma each year. Surgery is the main treatment for most orthopedic diseases. However, due to the complex structure, deep location and rigid bone tissues, it is very difficult for surgeons to operate with bare hands, which brings an opportunity for the development of orthopedic surgical robot. Based on this, the world’s top five orthopedic medical device companies (Stryker, JNJ, Medtronic, etc.) all invested heavily in the acquisition of orthopedic surgical robot companies and made great efforts to develop them. China successfully performed its first orthopedic surgery with medical robot many years ago. With the support of national policies, a number of research institutes and hospitals have worked together to promote the development of orthopedic surgical robots in China. At present, many orthopedic robot enterprises and products have been produced. TINAVI Medical Technologies Co., Ltd., now one of Chinese most successful orthopedic surgical robot companies, has been listed on SSE STAR MARKET with a market value of nearly 30 billion yuan. Its third-generation orthopedic surgery robot, TIANJI® Robot, has been successfully applied by hospitals in different regions. In the future, orthopedic surgical robot will become more intelligent and personalized, so as to further improve the overall treatment in China.
Professor Yu Wang, Vice Director of Medical device Research Institute, Director of Zhongguancun Open Laboratory of Medical Devices and Rehabilitation Technical Aid, Beihang University
Dr. Yu Wang, associate Professor & Assistant Dean, School of Biological Science and Medical Engineering, vice Director of Medical device Research Institute, and director of Zhongguancun Open Laboratory of Medical Devices and Rehabilitation Technical Aid, Beihang University. He participated in the development of the first domestic orthopedic surgical robot system of China, completed the first domestic robot-assisted orthopedic surgery and the first remote orthopedic surgery. This achievement has successfully achieved industrial transformation. It is now the only domestic orthopedic surgical robot that has obtained CFDA product registration. This robot system has been widely used in 35 hospitals in 3700 cases of surgery covered around 17 provinces/ municipalities/ autonomous regions. He jointly applied for the Hong Kong Innovation Technology Fund project and developed the active and passive hybrid orthopedic surgical robot with the Chinese University of Hong Kong. The system has been successfully applied in clinical practice and has been granted one US patent. He participated in the NIH RO1 project of the United States, and cooperated with Johns Hopkins University to carry out the research on hard tissue cutting technology based on continuum robot, and the related results were jointly published at the top international conference of robotics and biomedical Engineering. Watch his presentation here:
S4.5 Social Robots for Independent Living
Robots come in many different flavors. Apart from industry robots that have dedicated tasks without human interference, service robots are being developed that need to operate together with people. One example of such a robot is the social robot for independent living. As these social robots are becoming increasingly autonomous, they will require meaningful interaction capabilities to ensure efficiency and performance. We argue that understanding human intentions and communicating a robot’s own intentions are necessary requirements for fluent and efficient interactions. In this presentation, we will discuss three themes that are central for social robots for independent living. The first theme is navigation. When robots are applied in settings where they share a physical space with humans, they need to not only be able to navigate autonomously, but also do this in a socially acceptable manner. They need to take personal space into account and understand other social conventions. The second theme is social cues. When navigating in an environment in which humans are present, robots need to be able to communicate where they are going to make their behavior predictable. While interacting with people, they also need to be able to communicate that they understand what the human wants and respond appropriately. Robots can make use of social cues to achieve this. The third theme is social bonding. When having daily interactions with humans, robots need to be able to keep a record of their previous interactions, refer to them, and show that they remember what happened the previous day. This makes people more likely to attribute emotions and a personality to the robot, gives them the feeling that they are understood, and allows them to create a social bond with the robot that is helping them to live independently at home for longer.
Peter Ruijten, Assistant Professor on Social AI at Eindhoven University of Technology (TU/e).
Peter’s research topics include: Social HRI, Perceptions of human-likeness in technology, Trust in Autonomous Vehicles, and Conversational Interfaces.
Raymond Cuijpers. Associate Professor of Cognitive Robotics and Human-Robot Interaction at Eindhoven University of Technology (TU/e).
Raymond’s research topics include: Socially intelligent robots, Artificial intelligence for cognitive agents, Visual and haptic perception, and Human Motor Control.
Watch the presenation here:
S4.6 Development of automatic ultrasound robots for diagnosis and procedure guidance
With the recent advancements of medical robotic techniques, the alternative way to use conventional medical imaging systems could greatly improve the accessibility of the technique. With the joint efforts from King’s College London and Chinese Academy of Sciences, Institute of Automation, we’ve developed several intra-operative and extra-corporeal ultrasound robots to reform ultrasound acquisitions. The design of such robotic systems are motivated by the challenges of manually holding and manipulating a probe: e.g. the challenge of finding standard ultrasound views required by clinical imaging protocols, the risk of repetitive strain injury, and also the requirement for experienced sonographers to be on-site. In this talk, the presenter, who is the main researcher of these systems, will share the story behind these robots and show the beautiful encounter of medical imaging and robotic intelligence.
Dr. Shuangyi Wang Associate Professor at the Chinese Academy of Sciences, Institute of Automation (CASIA) (China)
Dr. Shuangyi Wang, associate Professor at the Chinese Academy of Sciences, Institute of Automation (CASIA). He received his PhD from King’s College London (KCL) on developing the world’s first robotic trans-esophageal ultrasound system. He continuously worked as a Post-doctoral researcher in KCL on developing advanced intelligent fetal diagnostic robots. He is supported by the CAS Talents grants and has been working on the projects funded by the EPSRC, Wellcome Trust, HTC, NSFC across UK and China and took the leading role in developing several robotic systems used in different medical scenario. Meanwhile, he has been actively involved in scientific research and published research papers on medical robotics in top journals and conferences, e.g. IEEE RA Magazine, IEEE TBME, Micromachines, MICCAI, IROS, TAROS. His current research interests include medical robotics, robotic ultrasound, computer-assisted interventions, and robotic-assisted diagnosis. Watch his presentation here:
S4.C1. Collaboration ePoster Pitch: Guardian: Social robots in long term care
With a decreasing workforce of care professionals and to support an active and positive working life of informal carers, there is a need for assistive technologies at home such as social robotics. The GUARDIAN project introduces a social companion, which aims to be of direct benefit for three groups of end-users; frail seniors, informal carers (at work) and formal carers. A major challenge is that informal caregivers find it increasingly difficult to continue their work in addition to the care tasks they already have. And when you also consider the increasing shortages in healthcare, the burden can become even higher. With Guardian, we therefore want to develop a robot companion with which the district nurse and informal caregiver can monitor his client, family or neighbour remotely. The GUARDIAN project follows from the start a unique iterative design, research and development methodology with 3 streams, focusing on: 1. Co-creation & Personalization; 2. Ethical and responsible innovation & design; 3. Business Modelling & Cost-effectiveness.
Herman Nap, Senior research, eHealth, Vilans, Visiting Senior Researcher, Human-Technology Interaction, Technical University of Eindhoven, The Netherlands
Mr. Henk Herman Nap, PhD, MSc is an expert on eHealth at Vilans and has a visiting research position at the Human-Technology Interaction group at Eindhoven University of Technology (TU/e). Vilans is the leading expertise centre on long-term care in The Netherlands. Henk Herman has a background in cognitive ergonomics, with a MSc degree in Psychology (Utrecht University), a PhD in Gerontechnology, and a Postdoc in senior gamers from the TU/e. Currently, Henk Herman works as a project coordinator and senior researcher in innovation & research, specifically in the field of eHealth and long-term care policies. At Vilans, Henk Herman is coordinator of the GUARDIAN Active and Assisted Living (AAL) research project on social robotics for people with dementia and (in)formal carers, the eWare AAL project on lifestyle monitoring and social robotics for people with dementia and their (in) formal carers, the MagicTABLE project (ToverTafel) and worked/s in other AAL projects such as Palette and FreeWalker as a workpackage manager of co-design and user evaluation. Furthermore, i-evAAlution, Certification-D and POSTHCARD are also AAL projects he supports in co-creation and research. Henk Herman is the WP3 leader of the H2020 ME-WE project on young carers. In the WP, a European Delphi study is performed in 6 countries and a systematic literature study. Henk Herman lead the research on the evaluation of the long-term care act in The Netherlands for the Ministry of Health. Besides these projects, Henk Herman is project leader of multiple eHealth implementation and evaluation projects in The Netherlands, such as ‘Anders Werken’ in the province of Noord-Brabant. In addition, Henk Herman is supervisor of several MSc students and is involved in the Human In Technology course at the TU/e. He is the co-promotor of Sandra Suijkberbuijk (PhD student TU/e & Vilans) on co-creation with people with dementia and Dirk Lukkien (PhD student UU & Vilans) on responsible AI in long-term care. Watch his presentation here:
S4.C2. ePoster: Estimation of Coronary Artery Movement Using a Non-Rigid Registration with Global-Local Structure Preservation
At present, coronary artery disease (CAD) is the leading cause of death in the world. Many studies have proved that CAD may highly correlate with the motion of coronary arteries. Cardiovascular imaging technology is widely used for the diagnosis of CAD. However, it cannot directly calculate the motion parameters of the heart and coronary arteries. Based on four-dimensional (4D) coronary computed tomography (CT) images, we propose a point set registration method with global and local topology constraints to quantify coronary artery movement. The global constraint is motion coherence of the point set to enforce the smoothness of the displacement field. The local feature descriptor-3D shape context (SC) is exploited to capture the local positional information of the point set. Moreover, the local linear embedding (LLE) based topological structure is designed to retain the local spatial distance of the point set. We embed these constraints into a maximum likelihood (ML) framework and derive the expectation-maximization (EM) algorithm to obtain the transformation function between the two point sets. The proposed method is compared with the current algorithms on the simulation data and tested in the real data, the experimental results demonstrate the effectiveness of the proposed approach for estimating the movement of coronary arteries.
Speaker: Bu Xu, Ph.D. Student, Northestern University, Shenyang, China
Bu Xu is pursuing her Ph.D. in Northeastern University, Shenyang, China. Her current research interests include the areas of medical image analysis, computer vision, and pattern recognition. Watch her presentation here:
S4.C3. ePoster: Estimation of Aortic Pressure Waveform Based on Multivariable Adaptive Transfer Function
This paper proposed and evaluated an adaptive transfer function (ATF) based on multivariable regression to estimate the aortic pressure waveform from the brachial pressure waveform. Synchronous invasive aortic and brachial pulse waveforms were recorded from 34 subjects for the validation of the proposed method. Individual transfer functions (ITFs) from the raw and normalized brachial pressure waveforms were trained using an autoregressive exogenous (ARX) model. Two generalized transfer functions (GTFs) were then derived: the first, by averaging the ITFs from the raw pressure waveform and the second, from its normalized counterpart. Multivariable adaptive transfer functions (MATFs) were then obtained by adjusting the gains of GTFs using multivariable regression formulas (MRFs) calculated from the ITFs and brachial hemodynamic parameters including systolic and diastolic blood pressures (SBP and DBP), pulse pressure (PP), mean blood pressure (MBP), cardiac output (CO) and heart rate (HR). The desired aortic pulse wave was reconstructed from the results of the two MATFs. Root mean square error (RMSE) and commonly adopted clinical hemodynamic indices including SBP, PP, form factor (FF), ejection duration (ED) and augmentation index (AIx) were used to evaluate the performance of the proposed method. The RMSE and the error of SBP and PP were 3.68±1.50, -0.08±4.18 and -1.14±3.91 mmHg, respectively. The error of ED was -1.25±3.81 ms. The percentage errors of FF and AIx were -0.21±1.78% and -0.38±11.15%, respectively. It was concluded that the proposed method yielded better performance in estimating hemodynamic indices of aortic pressure waves than the GTF and ATF adjusted only by a single variable.
Speaker: Shuo Du PhD Candidate, college of medicine and biological information engineering, Northeastern University (China)
Shuo Du is a second year PhD candidate from college of medicine and biological information engineering, Northeastern University. Her current research interest is noninvasive estimation of central hemodynamic indices. Watch her presentation here:
S4.C4 Multi-viewpoint Optical Positioning Algorithm Based on Viewpoint Optimization
Can Yea,b, Bo Wua,b, Qiaoling Yanga,b, Linjia Haoa,b, Nan Zhanga,b,*
a School of Biomedical Engineering, Capital Medical University, Beijing 100069, China;
b Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application， Capital Medical University, Beijing 100069, China
* Corresponding Author. E-mail address: email@example.com
Multi-viewpoints optical location system is an effective solution to the problems of light occlusion in optical positioning. Moreover, it can reduce the blind areas in the operation and obtain a larger field of vision. In this study, multi-viewpoints optical positioning algorithm based on the optimal reconstruction accuracy is proposed to solve the problem of light occlusion in optical positioning. Firstly, a parallel multi-view camera array is established, and the unified coordinate system of multi-viewpoints is obtained through the camera calibration and coordinate transformation. Then, according to the position relationship and the occlusion between the optical marker and each viewpoint, viewpoints are selected to reconstruct the coordinates of the optical positioning markers. Finally, the position of the positioning device is located according to the positional relationship between optical positioning markers and the tip of the surgical instrument. The experimental results indicate that the proposed algorithm is able to accurately locate position of surgical instrument and track the surgical instrument in real-time when there exists light occlusion.
Speaker: Can Ye, Biomedical Engineering Department, Capital Medical University
Can Ye was an undergraduate student from 2013 to 2017 in Biomedical Engineering Department, Capital Medical University, Beijing, China, and received his bachelor’s degree in Biomedical Engineer in 2017. He is now a graduate student in Biomedical Engineering Department, Capital Medical University. His research interests include surgery navigation and medical image processing.
S4.C5. An algorithm for multi-viewpoints stitching of surgical field assisted by optical positioning technology
Qiaoling Yanga, b, Can Ye a,b , Nan Liang a,b , Bo Wua,b,* and Nan Zhanga,b
a School of Biomedical Engineering, Capital Medical University, No.10 Xitoutiao, Youanmenwai, Fengtai District, Beijing, China, 100069
b Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10 Xitoutiao, Youanmenwai, Fengtai District, Beijing, China, 100069
* Corresponding Author. E-mail address: firstname.lastname@example.org
Telemedicine can establish the relationship between clinical experts and patients to realize remote treatment and also can provide clinical teaching for medical students. The combination of multi-viewpoint stitching and VR technology can expand the scope of surgical field and fully present realistic immersive surgical scene for telemedicine. Due to the complex operating room environment and the limited field of view, the surgical field cannot be completely obtained. Moreover, there are many close-up objects with large deformation in the field of view. Thus, traditional feature based stitching algorithm is prone to stitching ghosting, object deformation and other artifacts. Based on Adaptive As-Natural-As-Possible (AANAP) algorithm, a multi-viewpoint stitching of surgical scene algorithm is proposed which assisted by optical positioning points. The Optical Positioning Algorithm with Motion Tracking (OPAMT) is adopted to obtain the strict one-to-one matching point pairs in high-attention area of the left and right views to overcome deficiency above. Then integrated with SIFT feature matching point pairs, more accurate local homography models are established. Experiments on simulated surgery scenes demonstrate that the proposed algorithm achieves a better performance than the other state-of-the-art stitching algorithms. It can effectively improve the subjective quality of surgical panoramic stitching result. In conclusion, it can expand the scope of surgical field and fully present realistic immersive surgical scene combined with VR for telemedicine.
Speaker: Qiaoling Yang, Biomedical Engineering Department, Capital Medical University
Qiaoling Yang was an undergraduate student from 2013 to 2017 with the Biomedical Engineering Department, Xuzhou Medical University, Xuzhou, China, and received her bachelor’s degree in engineer from Xuzhou Medical University in 2017. She is now a graduate student of the Biomedical Engineering Department, Capital Medical University School, Beijing, China. Her research current focuses on image stitching, point clouds registration. Watch her presentation here:
Friday October 23, 2020: AI and Data –Medical Innovation
Keynote 5: AI and New Medical Paradigm. Making it happen through KIWI Hospitals and Organizations
23.10.2020, 08:00-08:30 Brussels/ 14:00-14:30 Beijing
AI is promising to change healthcare. Care with AI is nonetheless problematic. Carefulness is key. Carelessness can be legally is costly. This presentation will explore some implications of AI in medicine, particular public liability issues, as well as changes to medical paradigm.
Finally, the concept of KIWI organizations is proposed and explained. This can frame and help better prepare hospitals and other organizations to harvest the benefits of digital health and AI in particular without neglecting the basics: knowledge, interoperability and wisdom.
Referenced paper translated in Mandarin: https://3fda73f1-2df5-4662-91b3-54ccfc1d068d.filesusr.com/ugd/4e713b_0341ed7e3b9544268a7701a14cd34f52.pdf
Henrique Gil Martins, Past President SPMS, Portuguese eHealth Agency, Ministry of Health
Henrique Martins has a Medical Degree, Internal Medicine Speciality a Master and PhD in Management, and is finishing his Masters in Law, studying Public Liability implications of AI in Health. He is a Medical Doctor and University Professor at a Medical School and two Business schools, teaching and researching in Digital Health, Leadership and Management education for Medical Students and Health Professionals. He is the past president of SPMS, Portugal’s Digital Health Agency, where he led National eHealth efforts for close to 7 years, and the former Member States co-chair of the EU eHealth Network, the highest policy body on eHealth in the Union. He now works as an Academic in two high-ranked business schools and one medical school, as CMIO of Hospital Fernando Fonseca, Lisbon and on individual consulting projects in Healthcare Transformation and Digital Health (www.henriquemartins.eu). Watch his presentation here:
S5 – Health technology in Living environments and the Internet of Things – Privacy, ethics, and Cyber-security
23.10.2020, 08:30-11:00 Brussels/ 14:30-17:00 Beijing
S5.1 The GATEKEEPER project: European-led platform for Smart and Healthy Living at home
GATEKEEPER is one of the Large-Scale Pilots for innovative digital solutions addressing early detection of risks and medical interventions for age-related conditions and diseases. The project pursues an open innovation approach fostering uptake of digital health solutions making use of artificial intelligence, big data and internet of things technological paradigms involving ca 50.000 users in 8 European and 3 Asian regions.
Giuseppe Fico, PhD, Assistant Professor in Biomedical Engineering, LifeStech research group, Universidad Politécnica de Madrid
Giuseppe is working with the most important stakeholders in healthcare (research academies and institutions, to large industries, SMEs and startups, public and private institutions, associations and societies) in flagship research, innovation, strategic and policy initiatives, aiming to improve outcomes in health and social care systems, such as the European Innovation Partnership on Active and Healthy Ageing, the European Institute for Innovation & Technology on Health, – the Health Technology Assessment and Clinical Engineering Divisions of the International Federation of Medical and Biological Engineering and the Alliance for Internet of Things Innovation. Currently he is the technical manager of the ACTIVAGE and GATEKEEPER (H2020, 857223) Large Scale Pilots (LSP), of the BD4QoL project on head and neck cancer (H2020, 875192), and coordinator of the EIT Health Living Labs and Testbeds programme. He is co-Chair of the WG5 Smart living environment for ageing well in the Alliance for Internet of Things Innovation (AIOTI) and Councilor for Entrepreneurship and Innovation of the European Alliance for Medical and Biological Engineering & Science. He is lecturer of courses in Biomedical Engineering and Telecomunication Engineering at UPM. He is author of more than 60 research work in internationally recognized journals and conferences, committee member of scientific conferences of biomedical engineering societies and associate editor of the IEEE Journal of Biomedical Informatics and Frontiers in Digital Health for the Connected Health section. Watch his presentation here:
S5.2 Importance of Risk Stratification Strategy and Glocal Agile eHealth Development
Lou VWQ(1)(2), Cheng CYM(1) (1) Sau Po Centre on Ageing, The University of Hong Kong, (2) Department of Social Work & Social Administration, The University of Hong Kong, China
Background: Hong Kong will become a super-aged society with more than 21% of its total population will be aged 65 or above by 2024. With the longest life expectancy in the globe and advanced medical conditions, population ageing bring grand challenges as well as opportunities for sustainable development. In order to organise an open, value-driven and trust-based arena, it is time to bring together older adults, their family caregivers, healthcare professionals, service providers, start-up entrepreneurs and the business sector to achieve optimal independency of older adults. As a leading ageing research centre in Hong Kong, Sau Po Centre on Ageing took the lead to join the GATEKEEPER Project in 2019 as one of the Asian Pilots. Methods: Aiming to garner and steer eHealth development, four user cases have been formed using risk stratification strategy. Risk stratification is an ongoing process of screening and assigning patients according to their risk status and/or clinical complexity. The four user cases including i) a mobile app that serves as digital coach targeting ageing population with risk factors such as high-blood pressure, ii) a sensor-based digital monitoring station targeting older adults living along, iii) a community-based interoperable platform to analyse health characteristics and patterns of older adults via big data analytics, and iv) a team-based mobile app targeting stroke families, comprising with stroke literacy education and personalised rehabilitation and social support planning. Results: All the user cases were under development but impacted by COVID-19 pandemic, we acknowledge the importance of following a combination of glocal management and agile development to ensure a smooth progress. As one of the Asian Pilots, we believe the user cases can set good references to local as well as global evidence-based eHealth research.
Vivian WQ Lou, Sau Po Centre on Ageing, The University of Hong Kong, and Department of Social Work & Social Administration, The University of Hong Kong, HK, China
Dr Lou Vivian W. Q. is an Associate Professor at the Department of Social Work & Social Administration, The University of Hong Kong. She is also the Director of Sau Po Centre on Ageing, an Honorary Clinical Associate in the Centre on Behavioral Health, The University of Hong Kong. Dr Lou has studied widely on family caregiving, active aging, and their health impacts. Examining Chinese family caregivers’ mental health and financial impacts were pioneer studies that generated high impact publications. Recently, Dr Lou’s study extended to examining positive and/or resilient capacity of the family caregiving in Chinese context including studying secondary caregivers, social support, roles of domestic helper, and effective intervention strategies. Dr Lou also pioneered three mobile applications targeting volunteers, social workers, and stroke families respectively. She is now teaching social gerontology, clinical gerontology, and human development for both undergraduate and postgraduate students. She had publications in journals such as Aging and Mental Health, Family Process, Research on Social Work Practice, Journal of Human Behavior in the Social Environment, and Social Indicators Research. Dr Lou is presently a member of Society for Social Work Research, Hong Kong Social Workers Association, Hong Kong Association of Gerontology, and an international affiliate of American Psychological Association. Watch her presentation here:
S5.3 CyberSec4Europe – Aiming to safeguard values through excellence in cybersecurity
Cybersecurity is in a critical state in the European Union and its member states. While the long-term negligence of the need to secure ICT is a global phenomenon, there are specific European challenges: Most major ICT providers are not headquartered in Europe, European research results are often not monetized in Europe and the Cybersecurity landscape is still fragmented. To overcome this issue the European Commission has published a Proposal for a European Cybersecurity Competence Network and Centre. Cybersec4Europe (www.cybersec4europe.eu) is one of four projects piloting this network and some tasks of a centre. So CyberSec4Europe has the long-term goal of an EU able to secure and maintain a healthy democratic society, living according to European constitutional values (wrt e.g. privacy and sharing) and being a world-leading digital economy. CyberSec4Europe is rooting its work in 7 application domains, whose needs will confront the state of research to achieve better solutions where possible and a strategic roadmap for what is missing. It is following the intentions of European legislation that reflects and protects European societal, democratic and economic norms and principles such as data protection and privacy. CyberSec4Europe is organized as a research-based consortium working across four different but inter-related areas with a strong focus on openness and citizen-centricity in order to:
- Pilot a European Cybersecurity Competence Network;
- Design, test and demonstrate potential governance structures for the network of competence centres;
- Harmonise the journey from software componentry identified by a set of roadmaps leading to recommendations;
- Ensure the adequacy and availability of cybersecurity education and training as well as common open standards;
- Communicate widely and build communities.
This talk will introduce CyberSec4Europe and its major areas of work including the application oriented demonstrators. It will then especially introduce the demonstrator on medical data exchange.
Kai Rannenberg, Chair of Mobile Business & Multilateral Security at Goethe University Frankfurt
Kai Rannenberg holds the Chair of Mobile Business & Multilateral Security at Goethe University Frankfurt since 2002 and a Visiting Professorship at the National Institute for Informatics (Tokyo, Japan) since 2012. Until 2002, he was working with the System Security Group at Microsoft Research Cambridge on „Personal Security Devices & Privacy Technologies“. 1993-1999 Kai coordinated the interdisciplinary “Kolleg Security in Communication Technology”, sponsored by Gottlieb Daimler & Karl Benz Foundation researching Multilateral Security. In parallel he did his PhD at Freiburg University on IT Security Evaluation Criteria and the protection of users and subscribers. Before Kai had completed an Informatics-Diploma (Master) at TU Berlin with a focus on privacy, security, and distributed and real-time systems. Since 1991 Kai is active in ISO/IEC standardization in JTC 1/SC 27/WG 3 “Security evaluation criteria”. 2007 he became Convenor of SC 27/WG 5 “Identity management and privacy technologies”. In 2015/16 Kai Rannenberg served as the Chair of the Strategic Advisory Group on Industry 4.0/Smart manufacturing of the ISO Technical Management Board. Since October 2015 Kai is an IFIP Vice President; before he was an IFIP Councillor since 2009. Since 2014 he is Chair of the IFIP Publications Committee and Editor-in-chief of the IFIP Advances in Information and Communication Technology. From 2007 till 2013 Kai chaired IFIP TC-11 “Security and Privacy Protection in Information Processing Systems”, after having been its Vice-Chair since 2001. Kai is also active in the Council of European Professional Informatics Societies (CEPIS) chairing its Legal & Security Issues Special Interest Network (LSI) since 2003 and serving in its Board of Directors since 2019. From 2004 till 2013 Kai served as the academic expert in the Management Board of the European Network and Information Security Agency, and from 2013 till 2022 in ENISA’s Advisory Group (till 2019 named Permanent Stakeholder Group). Kai has been coordinating several leading EU research projects, e.g. the Network of Excellence “Future of Identity in the Information Society” and the Integrated Project “Attribute based Credentials for Trust” (ABC4Trust) and is coordinating CyberSec4Europe, a pilot for the European Cybersecurity Competence Network the EU is aiming for. Watch his presentation here:
S5.4 New Technologies and Innovation: Current Status and Directions in Beijing
Today we are facing the global challenge of limited healthcare resources and unlimited needs. AI could be a silver bullet to address this challenge. AI can be applied from different angle to almost all healthcare areas to create value added solutions, for examples in BDA, AI technology has been used in rehabilitation, in surgery products, in manufacturing processes and in preclinical studies, etc.
Professor Shanhong Mao, Capital Medical University of Beijing
Mao is a professor and PhD supervisor (part time) at Biomedical Engineering School of Chinese Capital Medical University. He also serves Chief Counsellor and Visiting Fellow of Chinese Academy of Inspection and Quarantine Sciences. With more than 30 years of experiences in healthcare innovation and business development, Dr. Mao served as Global Head of Mfg. Science and Technology at Alcon (Novartis), and various of executive positions in fortune 500 companies e.g. Bausch&Lomb and 3M. His expertise spans pharmaceuticals, medical devices, artificial intelligence in healthcare, and material science. Dr. Mao also served as adjunct professor at UTA, there he created the “Innovation and New Product Development” course to teach UT students new product development skills encompassing healthcare market analysis and segmentation, crossing innovation “valley of death” , product concept development, product design, mfg. process design and development, preclinical and clinical studies, regulatory approval, IP, business plan and project management. This class is brought to CCMU and currently taught at graduate student level. Dr. Mao has 15 patents, and 40 peer reviewed papers. He received his MBA from Carlson School of Business at University of Minnesota, PhD from UC Berkeley, MSc from Tsinghua University and BA from Peking University. Watch his presentation here:
S5.5 AI-driven Innovation
In health as in many other sectors, the next major step in innovation is and will be driven by data and AI. AI holds the promise to radically change the way people work, the quality of work and life, it also is a needed driver to increase productivity growth to fuel our economies to be able to deal with the aging population. AI is not new, it exists for several decades, but major increases in data availability, network capacity and computing power have accelerated developments. Not incremental but with a speed that was unimaginable even a few years ago. To focus on health, AI developments help efficiency, quality, and speed of all elements of a patient journey, from first complaints to treatment and revalidation, while significantly reducing costs and improving patient care and experience. There are however also barriers and threats, ethical considerations, including privacy concerns are well known and must be taken seriously for AI to become generally accepted and integrated into tomorrow’s healthcare. Policy must set the boundaries, but technology must find the solutions. A case for cardiovascular disease will exemplify how different approaches can be developed to comply with ethical standards and at the same time optimize a networked and distributed system.
Robbert Fischer, President, Knowledge for Innovation (K4I)
Robbert is President of Knowledge4Innovation, a platform in the European Parliament, and associated to the University de las Campinas in Brazil. Robbert is an innovation policy and strategy expert, currently focusing on AI in distributed environments in health. Having worked for the Commission DG CNECT as a seconded policy officer, he has also a 12 year experience as a management consultant for PwC in senior positions, and was managing director of the Joint Institute for innovation Policy (TNO, VTT, Joanneum Research and Tecnalia) for 8 years. He has led and worked on large assignments for the European Commission, the European Parliament, the EIB, national governments and private firms. Robbert has founded two start ups, of which one was a very early days big data analytics company. He holds diplomas and certificates from amongst others Oxford Said Business School (AI and Blockchain), Darden Business School (eBusiness) and Leiden University (law, with specialisations in IPR and informatics).Watch his presentation here:
S5.6 New Patterns of Rehabilitation Service in China in the Internet Era
Rehabilitation medicine is an important part of modern medicine. Public demand for rehabilitation has been an explosion in China not only because the growth rehabilitation needs of outpatients and discharged patients, but also because the accelerating population ageing further boosts the demand. Nevertheless, current system of rehabilitation in China is facing some problems, including the shortage and uneven distribution of medical resource, lack of standards, difficulty of inter-agency information sharing and lag in equipment and facilities. With the prosperous of the Internet Era, China has developed 3 patterns of “Internet+ Rehabilitation” services. The first one is the regional network pattern which is usually initiated by a regional government to establish a network of rehabilitation services including 3-tier medical institutions in its administrative area. The second one is the hospital-centered pattern which refers to a network of rehabilitation services with a large official general hospital as its core, and some other rehabilitation institutions of lower levels as the subordinate institutions. Its management and operation are generally market-oriented and only the involved medical institutions can share their patients’ health information. And lastly, just as its name implies, the third-party Internet platform pattern means that a third-party company offers its Internet platform to integrate rehabilitation service resources from different places and to efficiently match targeted services to patients who make their appointments in advance. Each pattern already has its mature cases after years of exploration and development in China. More innovations of “Internet+ Rehabilitation” are still on the way.
Speaker: Yuan Changrong, Professor of School of Nursing, FuDan University, Director of Research Center of Patient Experience
Yuan Changrong, Ph.D, Ph.D supervisor, Professor of School of Nursing, FuDan University, Director of Research Center of Patient Experience. She is the Fellow of American Academy of Nursing (FAAN), honorable professor of New York University (NYU), and she was appointed by PROMIS Health Organization (PHO) the position of official representative in China (PNC-China). Prof. Yuan also is Vice Chairman of Chinese Association for Life Care, Humanity Nursing Society; Vice chairman of Chinese Health Information and Big Data Association, Committee of Nursing; Vice Chairman of Shanghai Anti-Cancer Association, Committee of Cancer Nursing. Prof. Yuan held more than 20 funded research projects related to cancer nursing, nursing informatics and long-term care, including National Natural Science Foundation of China, Foundation of American Oncology Nursing Society and International Science and Technology Cooperation Program. 241 papers had been published by Prof. Yuan’s research team, including 43 SCI papers and she was a winner of 2012 Nobuo Maeda International Research Award of America Public Health Association (APHA), 2019 SUMMER AT CENSUS Scholar of U.S. Census Bureau and CANCER NURSING’s Annual Research Award for 2019. Prof. Yuan is the editorial board member of CANCER NURSING and expert of peer review of other 7 SCI journals. More than 40 Master, Ph.D and post-doctoral students had been successfully graduated with the guidance of Prof. Yuan. In 2018 and 2019, she was selected as a High-citation Scholar in China published by Elsevier. Watch her presentation here:
S5.C1 Collaboration ePoster Pitch: Gatekeeper: health technology for living environments and the Internet of Things
Gatekeeper was born from the necessity of providing a full standard and certified approach for data governance in healthcare, designed for the enhancement of data economy, providing solutions for data interoperability and re-use in machine learning (ML) and artificial intelligence (AI) algorithms ensuring data quality, protection, privacy and security. Based on standards like Web of Things and FHIR, Gatekeeper aims at providing a trustable digital platform centered on data and AI for healthcare that will be validate in different pilots in the EU and Asia. Furthermore, along with web technologies (such as REST-API and Web Socket), blockchain containerization and orchestration technologies (suck as Docker and Kubernetes) are used to build a resilient, scalable, secure and certified digital platform designed for data governance in the healthcare domain. At the current state within the Gatekeeper project there has been already defined a first release of the Gatekeeper digital platform, and a standard and common data space based on FHIR standard that will be populated by 8 pilot sites in Europe and 3 pilot sites in Asia. Furthermore, within the pilots, Gatekeeper aims to create an artificial intelligence ecosystem based on the concept of the Gatekeeper data space that will contribute to the HealthCare Data Space foreseen at European level, with the objective of providing services for early prevention and intervention in 7 Medical Reference Use Cases (RUCs) in order to improve the accessibility, effectiveness and sustainability of the healthcare systems. The aim of Gatekeeper project is to enhance collaboration worldwide, by promoting a collaborative environment based on fully standard, interoperable and open source solutions.
Giorgio Cangioli, Technical Lead, HL7 Europe and Chair HL7 Italy
Giorgio Cangioli is technical lead of HL7 Europe and the HL7 FAIR4FHIR Project. Senior Consultant of ICT in Health and Social Care, Degree in Physics, PhD in Energy Engineering, Master in ICT for Radiology. Giorgio has worked in the private sector as Production Manager, QMS Responsible, and R&D Responsible, and has extensive experience in ICT, standards and business process reengineering in health and social care. Giorgio is involved in several telemedicine; teleradiology, social care, primary care, Health Information Exchange, and eGovernment national and European. He assessed Regional eHealth projects for an Italian governmental agency. Giorgio led the epSOS Clinical and Semantic Experts Group and was the Trillium Bridge project manager. He served the HL7 Technical Steering Committee (2012; 2014-2015), authored of DICOM Supp 88, Chair of HL7 Italy.
Eugenio Gaeta, Gatekeeper platform lead, Universidad Politécnica de Madrid, Spain
Dr. Eugenio Gaeta (male) is PhD in Biomedical Engineering from UPM, Spain and Computer Science engineer from Universitá La Sapienza di Roma. His expertise is in wireless networks, wearable sensor, physical activity monitoring, machine learning and artificial intelligence, mobile Health platforms and Apps development. He has collaborated in several initiatives (R&D projects and collaboration with industry) in the areas of wearable devices, e- Inclusion, e-Health, mobile Health, AI, big data and multimedia. He was founder and CTO of Maketag s.r.l., an online video platform tool in order to create transmedia interactive videos for mobile and web. Maketag was acquired by BuzzMyVideos l.t.d. in February 2015. He is involved in the development of personalized and gamified platform for childhood obesity, as well as in the CI/CD environment within the Horizon 2020 project OCARIoT (GA 777082 / RNP 3007). Furthermore, he is involved in the Horizon 2020 project Plan4Act (H2020- 732266) that is a FET project that will record and understand predictive neural activity in no human primate and use it to proactively control devices in a smart house. Finally within the Gatekeeper project he is also working with the definition and development of an intelligent digital platform for health based the incoming W3C standard Web of Thing (WoT) and HL7 FHIR among others.
S5.C2 ePoster: Can we use AI to detect medical fraud? A pilot investigation
Background: Big Data Technologies have been applied in Healthcare sector providing significant advantages on both the quality of the healthcare services and the control of the cost. Specific Big Data analytic techniques can lead to effective fraud detection in the health domain. What we did? To analyze the data for fraud detection, prescription data of the following product categories were extracted: (i) Products and Supplies for Diabetes Management, (ii) Catheters, (iii) Medical Pads, and (iv) Special Nutrition Products and Supplements. For each of the above case, the date of the prescription, the prescriber, the specialty of the prescriber, the patient’s social security number, the product name, the category of the product, the product provider, and the location of the provider were selected. The aforementioned raw data were transformed in a manner to describe new features for each product based on the number of the cases. Specifically, the following features were calculated: (i) the number of the prescriptions, (ii) the number of patients who received each product, (iii) the number of prescribers who prescribe each product, (iv) the number of prescription to the number of patients ratio, (v) the number of prescription to the number of prescribers ratio, (vi) the number of the maximum prescriptions per patient, and (vii) the number of the maximum prescriptions per prescriber. A dataset of 879 products with 7 characteristics for each product was constructed. We used the Local Correlation Integral (LOCI) algorithm for outliers’ detection. LOCI was applied to detect any outliers on the dataset, producing some fruitful results. The results revealed that 7 out of 879 products could be characterized as outliers on the seven dimensional space. All of them had different behavior and space distribution, being further away from the rest of the data representing the other products. The 7 outliers may be assumed that have been associated with fraud, since at least two of them have been already confirmed as such by the EOPYY’s Auditing Services. According to the results of this study, the applied outlier detection approach can support and help the fraud detection process conducted by the auditing services in the Healthcare sector.
Spiros Georgakopoulos Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
Spiros V. Georgakopoulos was born in Argos, Greece, in 1987. He received a Bachelor in Mathematics and M.Sc. in “Computational Mathematics – Informatics on Education” from University of Patras, Greece, in 2010 and 2013 respectively. From 2019 he holds a Ph.D. in Computational Mathematics from the University of Thessaly, Greece. He is currently a Post Doctoral Researcher in the Department of Computer Science and Biomedical Informatics at University of Thessaly and Intelligent Systems consultant of Hellenic National Organization for the Provision of Health Services (EOPYY). His research interests include applications in machine learning, computer vision, bio-informatics, fraud detection in medical prescriptions and real-world problem solving. His published scientific work includes seven (7) journal papers and more than 15 international conferences papers. Dr. Georgakopoulos has received more than 200 citations on his published works. He has served as reviewer in journals with high impact factor (Neurocomputing, IEEE Transactions in Industrial Informatics, Neural Computing and Applications, Information Sciences etc) and member of program committee in many international conferences.
Parisis Gallos Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
Parisis G. Gallos received his Bachelor Nursing Degree in 2006, followed by a Masters on Health Informatics (2008) and he has related scientific work in this field until today. In 2016 became Doctor of Philosophy (Ph.D.) on Health Informatics with an orientation to m-Health Technologies for travellers. He is currently employed as an Expert Scientist and Visiting Lecturer at National and Kapodistrian University of Athens, Department of Nursing, and advisor to the President of the Hellenic National Organization for the Provision of Health Services – EOPYY. Former Visiting Lecturer at the University of Thessaly, Department of Computer Science and Biomedical Informatics, at the University of Peloponnese, Department of Nursing (2017) and at the Cyprus University of Technology from 2008 to 2010. His current Research Interests include Health Informatics, m-Health, Big Data in Healthcare, Healthcare Technology Evaluation, Biostatistics and Data Analysis, and he has a large Scientific Experience as a member of both Health Informatics Laboratory and Biostatistics Laboratory in the Department of Nursing, School of Health Sciences, National and Kapodistrian University of Athens. Parisis also has experience on EU Research Projects. He is an author of several scientific publications related to m-Health, Big Data in Healthcare and other research work. Additionally, he is an co-Editor and Reviewer in Scientific Publication Volumes of articles and he has been a member of different Organising and Scientific Committees of International and National Scientific Conferences. Since 2007, he has gained broad Teaching Experience in many taught modules covering the subjects of Health Informatics, Health Information Systems, Telemedicine, and Biostatistics. Finally, he is a member of the board (Publications Officer) of European Federation of Medical Informatics (EFMI), a vice President of the Greek Biomedical and Health Informatics Association and member of the board of the Greek Nursing Studies Association. Parisis is also an Emergency First Response.
Vassilis Plagianakos, Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
Vassilis P. Plagianakos, the EHFCN President, is the President of the Hellenic National Organization for the Provision of Health Services (E.O.P.Y.Y.) and a Professor of the Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece. He has served as the Head of the Department of Computer Science and Biomedical Informatics (2014-2019) and the Director of the postgraduate program Informatics and Computational Biomedicine of the School of Sciences (2014-2019). He is also the Founder of the Intelligent Systems Laboratory of the Department of Computer Science and Biomedical Informatics. Prof. Plagianakos is coauthor of more than 40 journal publications and more than 90 conference papers, and his published work has received more than 3000 citations. He has served as the Chair of the Board of the Hellenic Artificial Intelligence Society (2017-2019) and is a member of the IEEE Neural Networks Society and the IEEE Bioinformatics and Bioengineering Technical Committee (BBTC). His research interests are in the areas of Machine Learning and Neural Networks, Intelligent decision making, Evolutionary and Genetic Algorithms, Data Mining, Bioinformatics, Clustering, Parallel and Distributed computations, and real-world problem solving.
Watch the presentation here:
S5.C3: ePoster: Study on the Semi-supervised Learning-based Patient Similarity from Heterogeneous Electronic Medical Records
Similar patients selected based on EMR data, in conjunction with state-of-art machine learning algorithms, play an essential role in personalized medicine and clinical decision support. The patient similarity measurement is the core part of building personalized predictive models. However, EMR data is usually heterogeneous, making it more difficult than common homogeneous data to measure the patient similarity for further use. Therefore, we proposed a new semi-supervised learning-based patient similarity measurement to evaluate patients’ similarity for heterogeneous EMRs data. The predictive performance for four liver diseases prediction demonstrated the effectiveness and superiority of the proposed similarity measurement.
Speaker: Wang Ni is a second-year Ph.D. student at the School of Biomedical Engineering, Capital Medical University
Wang Ni is a second-year Ph.D. student at the School of Biomedical Engineering, Capital Medical University. She is interested in medical data mining, statistics, and the secondary use of EMRs data. Her studies aimed to facilitate personalized medicine and clinical decision support using patient similarity and machine learning algorithms based on EMRs data. Watch her presentation here:
Keynote 6: Current Situation and Development of Smart Care
23.10.2020, 12:00-12:30 Brussels/ 18:00-18:30 Beijing
The world and China are facing challenges from high morbidity and mortality due to non-communicable diseases (NCDs), aging of population, and outbreak of new pandemics such as COVID-19. Nurses and nursing care are playing central roles in managing NCDs, taking care of the elderly, and combating COVID-19. With the advancement of information and communication technology, rapid development of machine learning and deep learnings, artificial intelligence (AI) has brought attention to healthcare field including nursing care, especially in NCD management, elderly care, and COVID-19 prevention and control. This presentation will review the current state-of-the-art of AI in NCD management, elderly care, and COVID-19 prevention and control. Examples that utilized artificial intelligence in healthcare and nursing care in the above-mentioned areas, such as robots, individualized patient-centered lifestyle recommendation systems, will be presented. Weakness, barriers, and challenges, and future directions of applying AI in nursing care will be explored.
Dr. Ying Wu, Dean and Professor of Capital Medical University in Beijing, China
Dr. Ying Wu, Dean and Professor of Capital Medical University in Beijing, China. She is appointed by the China Ministry of Education as the Chair of the Working Committee for Accreditation of Nursing Education and Vice Chair of the Steering Committee for Higher Nursing Education. She is current serving on the Board of Director of International Council of Nurses and as the Vice President of Chinese Nursing Association. She created and Serves as the founding Chair of the Nursing Informatics Sub-Associate, Standardization Committee for Nursing Care, and Consortium of Nursing Innovation and Industrialization under the umbrella of Chinese Health Information and Big Data Association. She created and serves as the founding President of the Sub-Association of Smart Care for Elderly under the umbrella of Aging Well Association. She is the Past Vice President and past Regional Vice President of the International Medical Informatics Association and past President of the Asia Pacific Association for Medical Informatics. She has more than 30 years of cardiovascular (CVD) experience in nursing practice, Education and research. Her research areas including CVD acute care and behavior changes for CVD patients as well as elderly care, especially using mobile health technology. She is the principal investigator of four projects, one International (Regional) Collaborative Project, and one Key Project funded by the Natural Science Foundation of China and more than 10 projects from other funding sources with a total funding of over 8 million (approximately over one million US dollars). She is also the PI of a big research project “Development of a Virtual Hospital for Nursing Training and Performance Assessment” From Beijing Municipal Education Commission with a funding of 40 million RMB (approximately 6 million US dollars). She has more than 160 publications and is the editor or vice editor for 15 books and author of more than 20 books. She is the deputy editor or member on the editorial board of eight nursing professional journals including the Journal of Nursing Scholarship (ranked #3 in terms of impact factors among all 118 SCI indexed nursing journals worldwide). Watch her presentation here:
S6: AI in the Hospital of the future / Health and Wellness Technology
23.10.2020, 12:30-15:00 Brussels/ 18:30-21:00 Beijing
S6.1 Collaboration between business, healthcare and academia for the future health- some reflections!
Sweden’s ranking as the most innovation friendly country in EU is far from a co-incident. A long history of public-private partnerships, lifelong learning, open innovation platforms and support organisations as well as public funding has created a solid platform for innovation and risk-taking – also in Life Science. Multinational pharma companies such as AstraZeneca and Pharmacia stimulated an international exchange and collaboration with universities across the country which in turn attracted scientific talents from around the world. Sahlgrenska Science Park is part of this success, as an open arena with a mission to accelerate health innovation, and a vision of making West Sweden a world class life science region in 2030. I will talk about the necessary ingredients for making that vision a reality and give a few hands-on examples of successful cross-functional collaboration between healthcare, academia and industry.
Charlott Gummerson, CEO Sahlgrenska Science park, Sweden
Charlotta has extensive experience in managing companies and projects within the health and healthcare industry, and has previously held leading positions in the pharmaceutical industry such as GlaxoSmithKline and Astellas Pharma. She has also been responsible for investment management in early stage growth companies within life science and health at Almi Invest. Charlotta is member of the European Union MedTech Partnership Group, the International Association of Science Parks, Supervisory Council of Svenska Mässan, the Board of Connect Sweden West among other positions of trust. She is also member of the Jury of Nordic ehealthAward. Charlotta has basic medical training as a registered nurse specialized in surgery & medicine and has studied business & administration and leadership. Watch her presentation here:
S6.2 Practice of Mining Electronic Health Records
Among different types of data within EHRs, the narrative clinical text is the main form of communication within health care but also the most difficult for information extraction and computational analysis. The structuring and normalization of clinical notes is the foundation of mining EHRs, which are mostly based on challenging NLP techniques, such as medical named entity recognition, clinical relation extraction as well as entity normalization. We created specific tools to extract structure information from EHRs and leverage laboratory report automatically. With all the information extract from medical records, AI-based methods for analysis of clinical notes are creating unique opportunities to improve understanding of patient care while enabling better clinical decision making. Therefore, we further defined and investigated different types of NLP tasks for application in clinical practice. Computer-aided diagnosis, EHRs quality control, intelligent system performing quiet well in National Medical Licensing Exam just to name a few. In summary, our goal is to unlock the potential of EHR data with the support of NLP techniques for deriving better clinical insights and guidance.
Professor Ji Wu, deputy director of the Department of Electronic Engineering, Tsinghua University
Professor Ji Wu is deputy director of the Department of Electronic Engineering, Tsinghua University, Beijing, China. He received his B.S and Ph.D degrees from Tsinghua University, in 1996 and 2001, respectively, both in electronic engineering. He is heading the Multimedia Signal and Intelligence Information Processing Lab at Tsinghua University, and also the director of Clinical big data Center, Institute of Precision Medicine, Tsinghua University. Since 2006, he has been the director of Tsinghua-iFlyTek Joint Lab. He won the second prize of National Science and Technology Progress Award in 2011 and the first prize of Beijing Science and Technology Award in 2014. His research interests include speech recognition, natural language processing, pattern recognition, machine learning and data mining. He has been elected a Senior Member of the Institute of Electrical and Electronic Engineers in 2015. Watch his presentation here:
S6.3 Diagnostic Imaging for the Hospital of the Future
With Improved living standards and population aging, there is a strong drive for the technology innovations in health maintenance and healthcare solutions. Especially with post-pandemic awakening, people will re-examine the whole healthcare loop systematically. It will shape the future hospitals and healthcare systems. Medical imaging is an indispensable part of the modern medical practice. It will continuously be driven to new innovative levels, and in turn, drive the quality of the medical outcomes. Empowered by emerging technologies, such as AI+, 5G, IoT, etc., medical imaging will facilitate the future hospitals with optimized workflow, diagnostic accuracy, and therapeutical quality. This presentation will explore the current situation and trend of medical imaging technologies, and project a view of the future hospitals.
Professor Zhi Yang, Capital Medical University, Vice President of CMIA (China)
Zhi Yang, Ph.D, Professor and head of Bioinstrumentation Department and Medical Imaging Lab, School of Biomedical Engineering, Capital Medical University, and the Vice President of China Medical Informatics Association. His research interest include imaging physics, medical image analysis, and image guided surgical robotics. He has published a number of scientific research papers and holds multiple international patents. He served as a reviewer for several research journals and international conferences, including Medical Physics, IEEE TIP, and IEEE TMI, etc. His important research developments include the low dose CT reconstruction technique, known as AIDR 3D (Google scholar over 1500 search results), and image guided intervention technique (3D roadmap).
S6.C1 Collaboration ePoster Pitch: AI Enhanced Person-Centred Care in Stroke Rehabilitation
The person-centred care (PCC) in health care has been shown to advance concordance between care provider and patient on treatment plans, improve health outcomes and increase patient satisfaction. Since stroke is a chronic condition, the recommendations have to embrace the whole cycle of recovery, from the early treatment in the acute care hospital through reintegration into the community till the long term maintenance and prevention including social reintegration, health-related quality of life, maintenance of activity, and self-efficacy. Key challenges are in designing, developing and planning of PCC services for stroke rehabilitation in order to provide support to patient narratives, shared decision making, consistent evaluation framework. The potentials of using AI techniques/methods/approaches in PCC by exploring well-known approaches coming from different fields of applications and research communities, such as:
- Domain-specific knowledge representation with evidences from different resources, different representations and roles; adoption of well-known algorithms to support decision making;
- AI use for exploring data (data centric)- Despite the increasing availability of medical data from different sources, most of the applications of AI in the medical field are still mono-modality (utilizing one type of data at the time).
The Project is funded by Ministry of Science of Montenegro, period: 2019-2021. Currently, we applied model-driven approach for specification of commonalities and variabilities in domain, showing the following advantages: (i) re-use of the model for PCC service design in different institutions, (ii) measure changes over time due to the factors, having impact to the delivery of care (iii) measure impacts to quality of care and delivery of services to stroke patients, (iv) respecting patient’s and family’s opinion about his/her health and suggested health services. The created model is elaborated by using the database for experimental research which consists of registered stroke cases of patients (944 records of different patient, age from 13 to 96 years).
Opportunities for Cooperation with China can be focused on sharing experience and collaborative work on designing PCC services, refining the model, as well as applying some of alternative concepts/approaches.
Associate Professor Ivana Ognjanović, Univeristy of Donja Gorica, Montenegro
Ivana Ognjanović, PhD is Associate Professor at University of Donja Gorica, Montenegro. By having background in applied mathematics and PhD degree in software engineering (co-supervised by Prof. Dragan Gašević, Canada Chair in semantic technologies), her R&D activities are focused on application of AI in different fields of medicine. She was involved in realization of tens of EU funded projects, funding scheems: TEMPUS, SEE-ERA.Net project, Erasmus+, IPA, EUREKA and H2020. Now she is coordinator of Erasmus+ project which is aimed on digitization in medicine and industry 4.0 development, and she has Post-doctoral position at national scientific project related to AI application in neurology. In June 2020, she became a yEFMI (Young EFMI) WG chair. She is also a member of the Center of young scientists at Montenegrin Academy of Sciences and Arts (since 2014). Watch her presentation here:
S6.C2 Collaboration ePoster Pitch: European Electronic Health Record Exchange Format: Prospects for the hospital of the future in X-eHealth
The European Commission published its Recommendation on a European Electronic Health Record exchange format (C(2019)800) on 6 February 2019 [see 1 below], and the X-eHealth project  started on September 1, 2020, coordinated by Shared Services for Ministry of Health with 34 participating organizations from including HL7 Europe, IHE Europe, and CEN to advance specification for: Lab observations and results, Diagnostic Imaging observations and results, Discharge reports, Rare diseases, in order to  evolve the existing European eHealth services – Patient Summaries, ePrescription and eDispensation. The ultimate aim of the project is to reposition interoperability standards as infrastructure of innovation, by enabling the creation of high-quality data sets as towards a European Health Data Space initiative . In this way the three key priorities under the Communication on enabling the digital transformation of health and care  in the Digital Single Market empowering citizens and building a healthier society: (1) for Citizens’ secure access to their health data, also across borders – enabling citizens to access their health data across the EU; (2) Personalised medicine through shared European data infrastructure – allowing researchers, public health entities and other professionals to pool resources (data, expertise, computing processing and storage capacities) across the EU; (3) Citizen empowerment with digital tools for user feedback and person-centred care – using digital tools to empower people to look after their health, stimulate prevention and enable feedback and interaction between users and healthcare providers. Creating tools and facilitate adherence to interoperability standards will no doubt lead to high quality health data sets, by the use of coded and structured data contributing to standardisation and harmonisation of eHealth services in the EU  that can be shared across healthcare providers. Nonetheless, it will be important to facilitate interaction between patients and healthcare professionals, to support prevention and citizen empowerment . Take as an example, emergency departments of hospitals across Europe. They differ in size and organization structure. Agreeing on a minimal data set and guidelines on the use of Patient Summaries would very much increase our oversight their operation and needs, while providing citizens with superior services. This will no doubt contribute to better digital health services, and more sustainable health systems. At the same time, it will contribute to citizen and Health professional Empowerment and Engagement, making our neighborhoods and communities heathier and safer.
Our collaboration is for a global community of practice that will build capacity and knowledge on specifications for the exchange of EHRs, based on interoperability standards, by sharing tools, and insights on our successes and failures.
Catherine Chronaki, Secretary General, HL7 Europe and Vice President of EFMI
Catherine Chronaki is a computer engineer, vice president and president elect (2020-2022) European Federation for Medical Informatics, Secretary General HL7 Foundation. Catherine has played a key role within National and European digital health projects. Author of 100+ research papers, she has served as Associate Editor IEEE TITB, and on major eHealth conferences. Catherine served the HL7 Board (2008-2012), the eCardiology WG, European Society of Cardiology (2012-2015), and the eHealth Stakeholders group of the European Commission (2013-2022). She was coordinator of the Trillium Bridge and Trillium II on projects advancing adoption of international patient summary standards and eStandards that delivered a standardization roadmap for large scale eHealth deployment in Europe. In X-eHealth she co-leads the stream on Communities of Practice for the European Electronic Health Record Format aiming at the transition from Proof of Concept to large scale adoption and standards as Infrastructure for Innovation.
Diogo Martins, MD, Coordinator X-eHealth Project, SPMS, Portugal
Graduated in Health Equipment and Technology, academic background consists on Electronics, ICT & Medical Devices. Holds a MD in Healthcare Information Systems Management obtained in partnership between the Polytechnic Institute of Leiria (IPL) and Faculty of Medicine of Oporto (FMUP) with thesis a in eHealth field: “Impact of using mobile handheld technology in health care delivery: a Systematic review”. He has worked as Medical devices consultant, key account manager and as ICT Project Manager in SPMS, working on Healthcare data sharing – Radiology and DICOM Imaging; Infrastructure for Healthcare data sharing – XDS, IHE and Telemedicine Platform. Most recently he coordinates (X-eHealth, eHAction and HEALTHeID) and actively participates in more than 10 EU projects in the Digital Health related to Patient Empowerment, Innovative use of Health data, Interoperability and eHealth Sustainability. He has been able to make a “bridge” between ICT expertise and how important is to engage Healthcare professionals and citizens to ensure healthcare enhancements. Currently he works at SPMS and is responsible for the International Projects as well as and International Cooperation.
Company LinkedIn: https://www.linkedin.com/company/spms-epe/ Personal accounts: https://www.linkedin.com/in/diogo-martins-a6091344/ twitter: @diogorbmartins
Watch the presentation here:
SC6.C3. A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports
Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports. In this study, we designed an NLP pipeline for the direct extraction of clinically relevant features from Chinese radiology reports. The pipeline was comprised of named entity recognition, synonyms normalization, and relationship extraction to finally derive the radiological features composed of one or more terms. In named entity recognition, we incorporated lexicon into deep learning model bidirectional long short-term memory-conditional random field (BiLSTM-CRF), and the model finally achieved an F1 score of 93.00%. With the extracted radiological features, least absolute shrinkage and selection operator and machine learning methods (support vector machine, random forest, decision tree, and logistic regression) were used to build the classifiers for liver cancer prediction. For liver cancer diagnosis, random forest had the highest predictive performance in liver cancer diagnosis (F1 score 86.97%, precision 87.71%, and recall 86.25%).
Speaker: Honglei Liu Lecturer, School of Biomedical Engineering, Capital Medical University.
Honglei Liu received the Ph.D. degree in control science and technology from Tsinghua University, in 2016. She is now a Lecture with School of Biomedical Engineering, Capital Medical University. Her research interests include medical information, medical natural language processing.
Supporting Organizations and Projects
 I.Ekman et.al., Person-centred care- Ready for prime time, European Journal of Cardiovascular Nursing, 10(4):248-51, 2011
 Winstein CJ et al., Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association, Stroke 47 (2016).
 I Ognjanović, R Lewandowski, R Šendelj, D KRIKŠČIŪNIEN, J Eraković, Model Driven Approach for Development of Person-Centred Care in Stroke Rehabilitation, Studies in Health Technology and Informatics 272, 338-341