Axis 1: Knowledge extraction and discovery from healthcare data
The aim of this axis is to develop methods and tools for leveraging patients’ data in their wide variety and complexity. This encompasses the extraction and transformation of raw data into engineered featured and learned representations of good quality that will enable or facilitate the development of further clinical decision support and knowledge discovery approaches, as those developed in Axes 2 and 3.
Axis 2: Stochastic and supervised learning for clinical decision support
The objective of this axis is to propose original machine learning methods, including both statistical learning and deep learning approaches to account for low-sample-size high-dimension setting. In particular, we will consider modeling complex patient care trajectories for prognosis prediction and decision making, as well as, approaches using synthetic patient generation.
Axis 3: Data-driven and designs for next generation clinical trials
The objective of this axis is twofold and can be summarized as follows: how clinical trials can help machine learning? And inversely, how can machine learning help clinical trials? Accordingly, the first objective is to propose clinical trial methods adapted to continuous learning tools, as SaMD (Software as a Medical Device). The second objective, is to develop machine learning models and algorithms learning to enable innovative clinical trial designs using diseases and translational models, EHRs, clinical trial data and synthetic patients, for patient-related knowledge acquisition in biomedicine.
