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Nouvelle tache du challenge 5

Abstract

The medical field has always been attracted to the development of information technologies. More recently, recommendation systems (RS) in the health field have received more and more attention. These can be developed for two kinds of users: one the one hand, patients who can use these systems to become more actively involved in their own healthcare or, on the other hand, health professionals to help them with their clinical decisions. Health recommendation systems (HRS) aim to recommend diets, physical activities, doctors,... The development of HRS in pathology diagnosis, drug recommendation and other riskier areas is, however, currently challenged by the protection of private data, the sensitivity of medical data, and so on, that must be considered to guarantee the quality of the recommendations.
The objective of federated learning is to train a single model while keeping the data storage local on several different devices. In this way, the privacy of the data is fully preserved. In the hospital context, and more specifically in the context of HRS, the federated approach represents an important potential to face the challenges stated above.
The objective of this project is, at the end of the two-week workshop, to develop a functional federated health recommendation system based on real data obtained through CETIC. Moreover, a scientific article be written, describing the developed system. In parallel to this functional brick, our ambition is to develop a second brick using existing ontologies of the medical domain to infer new data from the received database.

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