Team presentation

The objective of the PreMeDICaL team (Precision Medicine by Data Integration and Causal Learning) is to develop the next generation of methods/algorithms to extract knowledge from health data and improve the care of patients. More specifically, the aim is to develop learning tools for personalized treatment effect prediction and for predicting outcome, while integrating different data sources to guide decisions made by clinicians and authorities. The aim is to push methodological innovation up to the stakeholders (patients, clinicians, regulators, etc.).

Premedical is a joint team between Inria and Inserm (Institut Desbrest d’Épidémiologie et de Santé Publique, Idesp, UA11, Unité Mixte de Recherche INSERM – Université de Montpellier) located in Montpellier and gathers different profiles: statisticians, biostatisticians, machine learners and clinicians and provides a unique opportunity for trans-disciplinary research and collaboration.PreMeDICaL team contributes to bridging the gap between fundamental research and its practical use and transfers its work through software development. 

Research themes

PreMeDICaL has two research axes:

  1.  Personalized medicine by optimal prescription of treatment.

We develop causal inference techniques for (dynamic) policy learning (allocating the best treatment for each person at the right time), that handle missing values and leverage both RCTs and observational data. Using both data sources allow to better design future RCTs and in the longer term to rethink the evidence needed to bring treatments to the market and to do so more quickly.

    2. Personalized medicine by integration of different data sources.

We build predictive models for heterogeneous data: for instance, given monitoring data in continuous time, images and clinical data what is the risk for an event to occur? Is it useful to have all the sources or do they provide the same information? We additionally develop solutions to handle missing values in a supervised learning setting and to improve the confidence of the outputs of the predictive models.

International and industrial relations

Industrial collaborations:  Adène, AdviceMedica, ALK,Capgemini Invent, Drago, Sanofi, 


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