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Keynote Lectures

Towards the Virtual Human Simulator
Giovanni Saggio, University of Tor Vergata, Rome, Italy

Academia and Industry: Partners in Leveraging Engineering, Science and Medicine for Clinical Translation
Elazer Edelman, Massachusetts Institute of Technology, United States

Ground-Truthing in the European Health Data Space
Mireille Hildebrandt, Vrije Universiteit Brussel, Belgium

Machine Learning Applied to Electronic Health Record Data: Opportunities and Challenges
Riccardo Bellazzi, Universita di Pavia, Italy

 

Towards the Virtual Human Simulator

Giovanni Saggio
University of Tor Vergata, Rome
Italy
http://www.hiteg.uniroma2.it/
 

Brief Bio
Giovanni Saggio received the M.Sc. and the Ph.D. degrees in Electronic Engineering from the University of Tor Vergata, Rome, Italy, in, respectively, 1991 and 1997. Since 1997 he's an Assistant Professor at the Department of Electronic Engineering, University of Rome “Tor Vergata”. His research interests include analog electronics, sensors, biomedical engineering, wearable devices, human-machine interface, brain-computer interfaces.


Abstract
The new technologies make it possible to acquire a considerable amount of data, for a considerable period of time. It follows the possibility of having the necessary and useful elements to characterize even rather complex systems. Acquisition, even prolonged, and characterization of data from a certain source (object, component, system, etc.) make it possible to create a virtual copy of this source, that is a digital twin (DT). DTs are already successfully adopted in electronics, mechanics, chemistry, but how far is the realization of digital twins of entire human bodies and which possibilities are opened to realize virtual human simulators?



 

 

Academia and Industry: Partners in Leveraging Engineering, Science and Medicine for Clinical Translation

Elazer Edelman
Massachusetts Institute of Technology
United States
 

Brief Bio

Elazer R. Edelman is the Edward J. Poitras Professor in Medical Engineering and Science at MIT, where he directs the Institute of Medical Engineering and Science. He is also Professor of Medicine at Harvard Medical School, and a cardiac intensive care unit cardiologist at the Brigham and Women’s Hospital (BWH) in Boston.

Edelman received Bachelor of Science degrees in Electrical Engineering and Computer Science and in Applied Biology, Master of Science Bioelectrical Engineering and Ph.D. in Medical Engineering and Medical Physics from MIT, and M.D. degree from Harvard Medical School. Graduate work with Robert Langer defined the mathematics of regulated drug delivery systems. After internal medicine training and clinical fellowship in Cardiovascular Medicine at BWH he was Research Fellow in Pathology at Harvard Medical School with Morris Karnovsky investigating the biology of vascular repair.

His research interests meld medical and scientific training leveraging pathophysiologic insight to improve clinical decision-making and device design. Studies of endothelial and vascular biology led to discovery of the mutable dynamic of endothelial state and importance in regulation of vascular diseases and cancer. His group reasoned that optimal control of biologic events recapitulated natural regulation. Hence, polymeric controlled drug delivery systems should mimic natural release and vascular implants devised with intimate knowledge of injury they induce. Perivascular and stent-based drug delivery, mechanical organ support and percutaneous heart valves are examples of the former, and therapeutic tissue engineered endothelial cell constructs of the latter.

More than 350 students and fellows have passed through Edelman’s laboratory publishing over 900 scientific articles and 90 patents.

Edelman is fellow of American College of Cardiology, American Heart Association, Association of University Cardiologists, American Society of Clinical Investigation, American Institute of Medical and Biological Engineering, American Academy of Arts and Sciences, National Academy of Inventors, National Academy of Medicine, and National Academy of Engineering. As Chief Scientific Advisor of Science: Translational Medicine he has set the tone for the national debate on translational research and innovation. As co-founder of ASTM F04.03 he helped create standards for cardiovascular implants. He served on FDA’s Science Board and as ORISE fellow FDA EIR. For bringing cardiovascular translational research to an international level of excellence the Spanish Parliament and King awarded Edelman the Spanish Order of Civil Merit. Most importantly, Elazer is an avid ice hockey goalie, and with his wife Cheryl parents to comedian-writer Alexander, Olympic athlete AJ, and Austin.


Abstract
Any list of the most influential medical innovations over the past century highlights the basic premise that ideas are often conceived of and brought to proof-of-concept in universities and clinical and communal realization by industry. It is the translation from academia to industry that is the path to impact of ideas and the optimization of this translation is what speaks to operational efficiency of novel concepts especially in medical care. We have learned that one can stimulate and teach such translation and increasing collaboration between academia and industry in Portugal and the United States heralds the next major innovations. We will discuss the history of such linkages in appreciating the mechanistic basis of critical diseases and devising new therapies and how active binational collaboration has brought together partners from across science, engineering and medicine in the basic and applied aspects of academia and industry.



 

 

Ground-Truthing in the European Health Data Space

Mireille Hildebrandt
Vrije Universiteit Brussel
Belgium
https://lsts.research.vub.be/en/mireille-hildebrandt
 

Brief Bio
Hildebrandt is a Research Professor on ‘Interfacing Law and Technology’ at Vrije Universiteit Brussels (VUB), appointed by the VUB Research Council. She is co-Director of the Research Group on Law Science Technology and Society studies (LSTS) at the Faculty of Law and Criminology. She also holds the part-time Chair of Smart Environments, Data Protection and the Rule of Law at the Science Faculty, at the Institute for Computing and Information Sciences (iCIS) at Radboud University Nijmegen. Her research interests concern the implications of automated decisions, machine learning and mindless artificial agency for law and the rule of law in constitutional democracies. Hildebrandt has published 5 scientific monographs, 23 edited volumes or special issues, and over 100 chapters and articles in scientific journals and volumes. She received an ERC Advanced Grant for her project on ‘Counting as a Human Being in the era of Computational Law’ (2019-2024), that funds COHUBICOL. In that context she is co-founder of the international peer reviewed Journal of Cross-Disciplinary Research in Computational Law, together with Laurence Diver (co-Editor in Chief is Frank Pasquale). In 2022 she has been elected as a Fellow of the British Academy (FBA).


Abstract
In this keynote I will discuss the use of health-related training data for medical research in light of the EU Health Data Space. If such data is deployed as a proxy for 'the truth on the ground', we need to address the issue of proxies. Ground truth in machine learning is the pragmatic stand-in or proxy for whatever is considered to be the case or should be the case. Developing a ground truth dataset requires curation, that is a number of translations, constructions and cleansing. What if the resulting proxies misrepresent what they stand for and what if the imposed interoperability of health data across the EU affects the quality of the data and/or their relationship to what they stand for? I will argue that ground-truthing is an act rather than a given, that this act is key to machine learning and assert that this act can have potentially fatal implications for the reliability of the output. Deciding on the ground truth is what philosophers may call a speech act with performative effects. Emphasising these effects will allow us to better address the constructive nature of the datasets used in medical informatics and should help the EU legislature to take a precautionary approach to medical informatics.



 

 

Machine Learning Applied to Electronic Health Record Data: Opportunities and Challenges

Riccardo Bellazzi
Universita di Pavia
Italy
 

Brief Bio
Riccardo Bellazzi is Full Professor of Bioengineering and Biomedical Informatics at the University of Pavia. He is the Director of the Department of Electrical, Computer and Biomedical Engineering of the University of Pavia. Moreover, he leads the Laboratory of biomedical informatics at the hospital “Salvatore Maugeri” in Pavia. 
The scientific interests of Prof. Bellazzi are highly interdisciplinary and are aimed at applications of informatics to medicine and life sciences, comprising artificial intelligence, biomedical data mining, telemedicine, temporal data analysis, decision support, clinical research informatics. He has a longstanding experience in ICT applications for Diabetes management.
Prof. Bellazzi has a wide and internationally recognized research activity.  In 2000 he founded the working group on "Intelligent Data Analysis and Data Mining" of the International Association of Medical Informatics (IMIA). In 2009 he became a Fellow of the American College of Medical Informatics for his international achievements. He is a Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI), and he was Vice-President for Medinfo of IMIA in the period 2011-2014. He is and was involved in several EU-funded projects related to IT in medicine and bioinformatics.
He is a member of the editorial board of the journals "Methods of Information in Medicine", "Journal of the American Medical Informatics Association", "International Journal of Biomedical Informatics", "Journal of Diabetes Science and Technology "and former Associate Editor of the "Journal of Biomedical Informatics". 
Prof. Bellazzi is author of more than 230 publications on international peer reviewed journals and of than 250 publications in proceedings of international conferences
Finally, he is co-founder of the academic spin-offs Biomeris, which implements software to support clinical research, and Engenome, which is specialized on the analysis of Next Generation Sequencing data with AI approaches.



Abstract
The increasing success of application of machine and deep learning in many areas of medicine, in particular in imaging diagnostics, is pushing towards the implementation of AI-based approaches to extract knowledge from other data sources, such as health records data (EHR). Examples include COVID-19 cooperative international efforts, such as the 4CE initiative. However, EHR data are particularly complex, due to their multifaceted nature and inherent relationship with the health care organizations generating the data. In this talk these challenges will be discussed through some examples and a few suggestions will be given for future research in this area.



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