CHARON Clara

PhD student at Sorbonne University
Team : DECISION
https://lip6.fr/Clara.Charon

Supervision : Patrice PERNY

Co-supervision : WUILLEMIN Pierre-Henri, BELMIN Joël

Probabilistic Classification for the Prediction and Explanation of Adverse and Avoidable Health Events in Nursing Homes

Nursing homes, which provide housing for dependent elderly people, are an option used by a large and growing population when, for a variety of reasons, including health, it is no longer possible for them to live at home. With the development of new information technologies in the health sector, an increasing number of health care facilities are equipped with information systems that group together administrative and medical data of patients as well as information on the care they receive. Among these systems, electronic health records (EHRs) have emerged as essential tools, providing quick and easy access to patient information in order to improve the quality and safety of care. We use the anonymized data of the EHRs from NETSoins, a software widely used in nursing homes in France, to propose and analyze classifiers capable of predicting several adverse health events in the elderly that are potentially modifiable by appropriate health interventions. Our approach focuses in particular on the use of methods that can provide explanations, such as probabilistic graphical models, including Bayesian networks.
After a complex preprocessing step to adapt event-based data into data suitable for statistical learning while preserving their medical coherence, we have developed a learning method applied in three probabilistic classification experiments using Bayesian networks, targeting different events: the risk of occurrence of the first pressure ulcer, the risk of emergency hospitalization upon the resident's entry into the nursing home, and the risk of fracture in the first months of housing. For each target, we have compared the performance of our Bayesian network classifier according to various criteria with other machine learning methods as well as with the practices currently used in nursing homes to predict these risks. We have also compared the results of the Bayesian networks with clinical expertise.
This study demonstrates the possibility of predicting these events from the data already collected in routine by caregivers, thus paving the way for new predictive tools that can be integrated directly into the software already used by these professionals.

Defence : 07/09/2024

Jury members :

Sébastien DESTERCKE, Directeur de recherche, Université de Technologie de Compiègne [rapporteur]
Maturin TABUE-TEGUO, PU-PH, CHU de Martinique, Université des Antilles [rapporteur]
Véronique DELCROIX, Maître de Conférences, Université Polytechnique Hauts-de-France
Marie-Jeanne LESOT, Professeure, Sorbonne Université
Patrice PERNY, Professeur, Sorbonne Université
Joël BELMIN, PU-PH, Hôpital Charles Foix - GHU APHP, Sorbonne Université
Pierre-Henri WUILLEMIN, Maître de Conférences, Sorbonne Université

Departure date : 07/31/2024

2021-2024 Publications