Keynotes
Keynote Speakers
Dimitrios I. Fotiadis
Prof. Dimitrios I. Fotiadis (Male), received the Diploma degree in chemical engineering from the National Technical University of Athens, Athens, Greece, and the Ph.D. degree in chemical engineering and materials science from the University of Minnesota, Minneapolis. He is currently a Professor of Biomedical Engineering in the Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece, where he is also the Director of the Unit of Medical Technology and Intelligent Information Systems, and is also an Affiliated Member of Foundation for Research and Technology Hellas, Biomedical Research Institute. He was a Visiting Researcher at the RWTH, Aachen, Germany, and the Massachusetts Institute of Technology, Boston. He has coordinated and participated in more than 250 R&D funded projects (in FP6, FP7, H2020, and national Projects), being the coordinator (e.g. INSILC, TAXINOMISIS, HOLOBALANCE, CARDIOCARE, DECODE, etc.) and Technical coordinator (e.g. SMARTOOL, KARDIATOOL, TO_AITION, etc.). He is the author or coauthor of more than 300 papers in scientific journals, 500 papers in peer-reviewed conference proceedings, and more than 50 chapters in books. He is also the author/editor of 30 books. His work has received more than 23,000 citations (h-index=71). He is IEEE EMBS Fellow, EAMBES Fellow, Fellow of IAMBE, member of the IEEE Technical Committee of information Technology in Healthcare, Editor in Chief of IEEE Journal of Biomedical and Health Informatics, Member of the Editorial Board in IEEE Reviews in Biomedical Engineering, Associate Editor for IEEE Open Journal in Engineering in Biology and Medicine and Computers in Biology and Medicine. His research interests include multiscale modelling of human tissues and organs, intelligent wearable/implantable devices for automated diagnosis, processing of big medical data, machine learning, sensor informatics, image informatics, and bioinformatics. He is the recipient of many scientific awards including the one by the Academy of Athens. He is the co-founder of PD Neurotechnology Ltd, UK.
Keynote Title: Overcoming open issues and unmet needs in healthcare through the sharing, harmonization and federated analysis of unstructured medical data
The underlying heterogeneity and the reduced quality in the existing medical data across different clinical centers obscures the interlinking and co-analysis of such data. In addition, the legal and ethical barriers obscure the sharing of sensitive data and highlight the need for the development of federated learning strategies to enable the federated analysis of medical data across different countries with inherent data protection policies. Here we present a straightforward framework to overcome open issues and unmet needs in healthcare through the design and development of: (i) automated methods for data curation to address data inconsistencies and improve data quality in terms of relevance, conformity and completeness, (ii) hybrid data harmonization pipelines based on a combination of lexical and semantic matching methods with word embeddings which are utilized on top of medical index repositories and external knowledge bases to transform the heterogeneous data into a common standardized format, (iii) data augmentation through the design of robust virtual population generators to enhance the statistical power of databases with insufficient population size and improve the performance of the existing AI models, and (iv) federated AI algorithms to enable the training and evaluation of trustworthy and explainable AI workflows across high-quality and harmonized data stored in federated databases within a cloud environment. Multiple case studies are conducted across different clinical domains, including: primary Sjögren’s Syndrome (pSS) and hypertrophic cardiomyopathy (HCM), among others, to demonstrate the efficacy of the proposed framework to address clinical unmet needs.
Letizia Tanca
Letizia Tanca is a full professor at Politecnico di Milano (Dipartimento di Elettronica, Informazione e Bioingegneria) where she has held various institutional positions since 2001. She teaches Databases and Technologies for Information Systems and is the author of about 200 publications on databases, context-aware management of mobile data, semantic information and Big Data analysis. She has long been reviewer for the best international DB journals and conferences and is currently Associate Editor of PVLDB 2023 and future Program Chair of EDBT 2024. She has acted for 6 years in the Board of the association Informatics Europe, and more recently as Vice-President of the GII (Italian Group of Computer Engineering Academics). Currently, she coordinates her department’s group of the Health Big Data Project, an Italian project whose aim is to build a Federated Data Lake platform to support the research of 51 medical research institutions all over Italy.
Keynote Title: Supporting Medical Trials with a Data Lake Federation: a Research Perspective
The collection of data needed for clinical trials is very critical, since a complete picture of the patients’ status can only be obtained from real-world data, collected in different clinical institutions during their research and clinical history. To create this tremendous value, we need a suitable solution to store and process the huge amount of necessary information, often coming from very heterogeneous devices and data sources. Data lake technology appears to be a promising solution for achieving the ability to manage and analyze data in healthcare: we can rely on it to manage the complexity of the volume and variety of big data by providing data analysts with a self-service environment to which advanced techniques can be applied. Our research proposes the adoption of a data lake federation, through which the involved medical organizations obtain significant results and new benefits. The extremely heterogeneous data collected in the data lakes of the federation must be accurately described, in order to document its quality, facilitate its discovery and integration, define ethical, security and privacy policies. Based on the experience in the Health Big Data project, τηε proposed architecture to collect and use data in the federation, identifying the main IT research challenges we are facing.