Titre :

Self-adaptive agents based on reinforcement learning to optimize patient scheduling in emergency department

Auteur(s) :

Ajmi, Faiza [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université Catholique de Lille - Faculté de gestion, économie et sciences [UCL FGES]
Laboratoire Interdisciplinaire des transitions de Lille [LITL]
Ben Othman, Sarah [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ajmi, Faten [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Zgaya-Biau, Hayfa [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Renard, Jean-Marie [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Smith, Gregoire [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Hammadi, Slim [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la manifestation scientifique :

IEEE Conference on Systems, Man, and Cybernetic (SMC 2023)

Ville :

Honolulu, HI

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2023-10-02

Mot(s)-clé(s) en anglais :

Hybrid metaheuristics
Collaborative optimization
Multi-agent system
Reinforcement Learning
Q-Learning

Discipline(s) HAL :

Informatique [cs]/Système multi-agents [cs.MA]
Informatique [cs]/Apprentissage [cs.LG]
Computer Science [cs]/Operations Research [math.OC]
Sciences du Vivant [q-bio]/Ingénierie biomédicale

Résumé en anglais : [en]

In this article, we present a framework ABOS based on acollaborative multi-agent system (MAS) for multi-skill health care scheduling using metaheuristics. In this framework, each agent preforms his actions autonomously in ...
Lire la suite >In this article, we present a framework ABOS based on acollaborative multi-agent system (MAS) for multi-skill health care scheduling using metaheuristics. In this framework, each agent preforms his actions autonomously in the search space of a scheduling optimization problem. Information about patient scheduling is shared between agents who collaborate through the dynamic environment. The objective is to allow the agents to adapt their decisions using the Reinforcement Learning approach according to the acquired experience with the interaction with the other agents and the environment. The aim of this interaction between agents is to enhance the quality of the solutions provided by the agents from the search space. Experiments were performed using real data provided by the adult emergency department (AED) of Lille university hospital center (LUHC). The simulation results confirm that the integration of Machine Learning in agent behaviors impacts the quality of the scheduling solution. Collaboration between agents in ”friend” or ”enemy” mode influences the quality of the solution as well and thus impacts the health care patient pathway.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL