PORCHER Raphaël

Personalized medicine

raphael.porcher [at] aphp.fr

Raphael Porcher

Short bio

Associate Professor of Biostatistics at Université de Paris, co-director of the Centre Virchow-Villermé Paris Berlin, and member of the METHODS team of CRESS-UMR1153. Member of the Comité d’Evaluation Ethique / Institutional Review Board of Inserm. Senior Associate Editor for Methods at Clinical Orthopaedics and Related Research, and Associate Editor for Statistics, Artificial Intelligence and Modeling Outcomes at the Journal of Hepatology.

Topics of interest

Machine learning, prediction, personalized medicine, causal inference

Project in Prairie

Raphaël Porcher will work on methods to identify optimal individualized treatment strategies using real-life observational data, as well as principled approaches to evaluate their performance and potential impact. He will participate in the teaching and training of medical students in AI and machine learning, and in interdisciplinary programs on AI and health.

Quote

Artificial intelligence represents an unprecedented opportunity for clinical decision support in medicine, and decision upon treatments in particular. Observational data are also an invaluable source of information to learn about treatment efficacy, but the methodological aspects of therapeutic evaluation, and the issues of confounding and bias in particular, should not be overlooked, especially in complex, time-dynamic, settings. They are central for clinical applicability and impact. Bringing together strong methodology, theories on causal inference, artificial intelligence and large-scale real-life data has the potential to improve how patients are treated and ultimately population health.

MARIJON Eloi

Cardiology, Cardiac Arrhythmias, Cardiac Electrophysiology

eloi.marijon [at] aphp.fr / Twitter: @EloiMarijon

Eloi Marijon

Short bio

Dr. Eloi Marijon is a cardiovascular and cardiac electrophysiology specialist, heading the Cardiac Electrophysiology Section at the European Georges Pompidou (EGP) hospital in Paris. He is Professor of Medicine at University of Paris, Senior Researcher at the Paris Cardiovascular Research Center (INSERM U970, Cardiovascular Epidemiology), and codirects the Paris Sudden Death Expertise Center (SDEC).

Topics of interest & Project in Prairie

Research Program on Artificial Intelligence Applied to Cardiac Rhythm Disorders

Quote

Because efforts to improve prediction of individuals at long-term risk for sudden cardiac death (SCD) have been disappointing so far, we started an alternative approach aiming at pre-emptive risk stratification and prevention of SCD. We have focused on sentinel events preceding SCD, in light of recent advances in communication and remote transmission technologies, to eventually better identify patients at high risk of short-term SCD. Our main hypothesis is that it is possible to determine a high-risk group of subjects at risk of imminent SCD. Application of AI to continuous device monitoring of patients at high of ventricular fibrillation would allow the identification of a particular pattern prior the occurrence of the fatal event. National Consortium (DAI-PP program, centralized at HEGP) has just been initiated with a 2-year enrollment and a 5-year follow-up of more than 5000 patients with implantable cardioverter defibrillator and remote continuous monitoring. In addition, systematic collection of digital EKG in the field has been initiated part of the ongoing prospective Paris-Sudden Death Expertise Center registry, which collect all SCD since May 15, 2011. A better understanding of the electrical signal dynamics prior to the occurrence of ventricular fibrillation would allow a better triage and identification of high-risk patients.

BURGUN Anita

AI in medicine

anita.burgun [at] aphp.fr

Anita Burgun

Short bio

MD, PhD. Professor of Medical Informatics at Université de Paris. Chair of the Department of Medical Informatics at Georges Pompidou and Necker Hospitals (AP-HP). Leader of the «Information science to support personalized medicine» research group at Cordeliers Research Center.

Topics of interest

Medical decision, Electronic Health Records, Natural Language Understanding

Project in Prairie

Anita Burgun’s objectives are to develop AI systems that can be used to support medical decision in clinics, like hybrid approaches combining learning algorithms and logics. Examples are deep phenotyping based on the EHR, similarity metrics in rare diseases, and precision medicine. She will participate in AI programs for undergraduate and graduate medical students, as well as AI in medicine summer school.

Quote

The development of Artificial Intelligence for clinical decision cannot be achieved without hybrid approaches, to learn from a limited number of heterogeneous cases, with complex phenotypes, and complex underlying biological mechanisms. Solutions based on deep phenotyping are being investigated to solve a diagnostic or a therapeutic problem of a new patient by recalling previous cases that exhibited similar characteristics in rare disease clinical data warehouses. Future directions consist in developing digital twins solutions that integrate the precision medicine paradigm. Such approaches combine different methodologies, using holistic omics data, and existing data from clinical trials and routine care. Because of data complexity, multi-scale knowledge, and fast changing models, AI in medicine raises lots of issues that require multidisciplinary research at the highest level.

ALLASSONNIÈRE Stéphanie

Statistics for medical data

stephanie.allassonniere [at] parisdescartes.fr

Allassonnière Stéphanie

Short bio

Professor of Mathematics at the School of Medicine, University of Paris and associated Professor in the applied Mathematics department of Ecole
Polytechnique. Manager of master programs and masterclasses in AI in healthcare.

Topics of interest

Statistical modelling, stochastic optimization, MCMC samplers, medical data analysis

Project in Prairie

Stéphanie Allassonnière will focus on understanding diseases response to treatments and propose decision support systems for diagnosis and therapy. This will involve statistical modeling of clinical data, stochastic estimation in particular using mixed effect models. These research axes will require theoretical and methodological developments towards medical transfer. She will also be in charge of several programs as Bioentrepreneur master degree, IA masterclasses for physicians and co-develop the health track of the existing MVA master program.

Quote

Artificial intelligence is not well appreciated by medical doctors who fear being replaced by computers. By contrast, I believe that AI represents a unique chance for caregivers to assist them to better detect and care patients. For practitioners to take on this opportunity, we need to develop more robust and interpretable methods than the current state-of-the-art. This is what I will do within PRAIRIE by fostering multidisciplinary collaborations with Paris hospitals. I will also develop innovative training programs within the medical school to bridge the gap between computer scientists and physicians.