Visualizing Health Data – From Fundamental Research to Successful Applications
Roy Ruddle, University of Leeds, United Kingdom
Patient Innovation - When Patients Innovate and Improve Their Lives
Helena Canhão, Universidade NOVA de Lisboa, Portugal
Uncertainty Modeling and Deep Learning Applied to Food Image Analysis
Petia Radeva, Universitat de Barcelona, Spain
AI in Medicine: "Lessons Learned" from the 70s to the Present Day and Needs for New Synergies and Professional Roles
Silvana Quaglini, Dept. of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
Visualizing Health Data – From Fundamental Research to Successful Applications
Roy Ruddle
University of Leeds
United Kingdom
Brief Bio
Roy Ruddle is a Professor of Computing at the University of Leeds (UK), and an Alexander von Humboldt Foundation and Alan Turing Institute Fellow. He has worked in academia and industry, and researches visualization, visual analytics and human-computer interaction in spaces that range from high-dimensional data to virtual reality.
Abstract
Health data comes in many forms, from relational databases of electronic health records, to genomic sequencing and the tera-pixel collections of images that pathologists use to diagnose diseases such as cancer. This talk will take you on a journey across all of those modalities, connecting fundamental research with successful applications. One of those applications allows users to investigate and explain data quality problems that involve dozens of variables, and others exploit the gigantic display real estate of Powerwalls to speed up research and diagnosis of cancer (Orchestral and the Leeds Virtual Microscope, respectively). Most people think that visualization is only needed to analyse data and present findings. However, in the big data era the most important use of visualization is for designing models and data processing pipelines, so I will conclude with examples involving AI.
Patient Innovation - When Patients Innovate and Improve Their Lives
Helena Canhão
Universidade NOVA de Lisboa
Portugal
http://cedoc.unl.pt/epidoc-unit/
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Brief Bio
Graduate, PhD and Habillitation in Medicine from Faculty of Medicine, University of Lisbon, Portugal. She also holds a Master's Degree in Clinical Research from Harvard Medical School, Harvard University, USA.Head of EpiDoC Research Unit, CEDOC. Coordinator of CHRC, NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal.She is Full Professor of Medicine at NOVA Medical School, Invited Full Professor at National School of Public Health and Head of Rheumatology Unit, CHLC- Hospital Curry Cabral, Lisbon.She is board member of the Council of Universidade NOVA de Lisboa, and board member Council of NOVA Medical School, UNL.Elected President of the Portuguese Society of Rheumatology, President of the EpiSaude Association and Co-leader and Chief Medical Officer for the Patient Innovation Project.She chairs the Advisory Board of Value4Health CoLab and coordinates NOVA Saude Ageing Group. She authored and co-authored more than 200 peer-reviewed publications and edited 5 books and 25 book chapters in the fields of medicine, rheumatology, innovation and clinical research.
Abstract
User-innovation is a new field of research and interest. Users often innovate and develop new solutions in a variety of fields. However, health with all the issues involving ethics, regulations, safety and scientific knowledge would not be an easy field for patients or informal caregivers innovate. But we found that, in fact, patients innovate, some becoming even entrepreneurs, however the diffusion of their solutions is low. Our open, non-profit Patient Innovation platform shares around 1000 solutions developed by patients and caregivers, from simple to high-tech solutions. The solutions are published only after medical screening and each one has a story behind.
Uncertainty Modeling and Deep Learning Applied to Food Image Analysis
Petia Radeva
Universitat de Barcelona
Spain
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Brief Bio
Prof. Petia Radeva is a Full professor at the Universitat de Barcelona (UB), Head of the Consolidated Research Group “Artificial Intelligence and Biomedical Applications (AIBA)” at the University of Barcelona. Her main interests are in Machine/Deep learning and Computer Vision and their applications to health. Specific topics of interest: data-centric deep learning, uncertainty modeling, self-supervised learning, continual learning, learning with noisy labeling, multi-modal learning, NeRF, food recognition, food ontology, etc. She is an Associate editor in Chief of Pattern Recognition journal and International Journal of Visual Communication and Image Representation. She is a Research Manager of the State Agency of Research (Agencia Estatal de Investigación, AEI) of the Ministry of Science and Innovation of Spain. She supervised 24 PhD students and published more than 100 SCI journal publications and 250 international chapters and proceedings. Petia Radeva belongs to the top 2% of the World ranking of scientists with the major impact in the field of TIC according to the citations indicators of the popular ranking of Stanford. Moreover, she was awarded IAPR Fellow since 2015, ICREA Academia’2015 and ICREA Academia’2022 assigned to the 30 best scientists in Catalonia for her scientific merits, received several international and national awards (“Aurora Pons Porrata”, Prize “Antonio Caparrós” ).
Abstract
Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis.
AI in Medicine: "Lessons Learned" from the 70s to the Present Day and Needs for New Synergies and Professional Roles
Silvana Quaglini
Dept. of Electrical, Computer and Biomedical Engineering, University of Pavia
Italy
www.labmedinfo.org
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Brief Bio
Silvana Quaglini is Full Professor of Health Care Information Systems Università degli Studi di Pavia, Italy, where she teaches Medical Informatics and Decision Support in Medicine. Se is author of more than 200 articles in International Journals and Scientific Books. Her research interests regard decision support systems in medicine and more particularly basic areas such as decision theory, clinical process modeling, artificial intelligence, probabilistic systems, biomedical statistics, knowledge acquisition. Application areas include support systems for diagnosis, therapy and monitoring, such as computerized guidelines, economic evaluation models based on decision analysis, telemedicine systems and workflow management within healthcare organizations. The main medical areas covered by such applications are stroke, chronic diseases, cardiovascular risk, motor and cognitive rehabilitation. The recent push toward personalized medicine has focused the latest applications on the "shared decision making" and "context-aware home-monitoring patients
Abstract
A brief history of AI in medicine will be presented, through the illustration of the formalisms that have been developed to implement Decision Support Systems (DSS), from production rules, to probabilistic networks, ontologies, up to machine learning. The difference between the first DSSs, typically stand-alone, and the current ones, typically distributed and included in wider IT systems, will be highlighted. Attention will be focused on how Evidence-Based Medicine (EBM) influenced the development of DSSs, and on the (apparent?) dichotomy between EBM and the more recent paradigm of Personalized Medicine. Finally, a nod to medico-legal/ethical problems related to the use of DSSs in the GDPR era will be provided.