Federated Health Recommender System

Sarah Pinon, Simon Jacquet, Colin Bulcke, Edouard Chatzopoulos, Xavier Lessage, Raphaël Michel

2023

Abstract

Precision Medicine is a new and growing approach to health care. This initiative includes different patient-oriented Decision Support Systems (DSS), such as Health Recommender Systems (HRS). These patient-oriented DSS aim to increase the accuracy and personalization of health care. However, the development of these systems faces a major obstacle related to the confidential and private nature of medical data. These systems require, indeed, a large volume of data to run effectively. But medical data are dispersed among several institutions and cannot be centralized for strict confidentiality reasons. To address this issue, this position paper proposes a system’s architecture in which Federated Learning is exploited to build a HRS. Federated Learning allows exploiting the data maintained by different institutions to build the system without requiring their sharing. To demonstrate the feasibility of our proposition, we build a Federated Drug Recommender System. The goal of the system is to assist doctors in their administration of drugs by using historical disease-drug interactions and drug data. As a position paper, the objective of this use case is limited to a proof of concept realized on non-sensitive open-source data. Our ambition is then to use the architecture proposed in this paper to develop a Federated HRS on real medical data.

Paper Citation

in Harvard Style

Pinon S., Jacquet S., Bulcke C., Chatzopoulos E., Lessage X. and Michel R. (2023). Federated Health Recommender System. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 439-444. DOI: 10.5220/0011722700003414

in Bibtex Style

@conference{healthinf23,
author={Sarah Pinon and Simon Jacquet and Colin Bulcke and Edouard Chatzopoulos and Xavier Lessage and Raphaël Michel},
title={Federated Health Recommender System},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={439-444},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011722700003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Federated Health Recommender System
SN - 978-989-758-631-6
AU - Pinon S.
AU - Jacquet S.
AU - Bulcke C.
AU - Chatzopoulos E.
AU - Lessage X.
AU - Michel R.
PY - 2023
SP - 439
EP - 444
DO - 10.5220/0011722700003414
PB - SciTePress