MIXED MACHINE LEARNING AND AGENT-BASED SIMULATION FOR RESPITE CARE EVALUATION

摘要

Respite care is a new service to decrease burnout risk of caregivers. Hospitalization related to caregivers burnout are costly and should be avoided. Pre-identification of caregivers with severe burnout is crucial to better manage respite care services through smart admission policies and health resources management. In this article we propose a mixed machine learning and agent-based simulation for respite care evaluation taking into account smart admission policies. Results show that neural networks approach demonstrate best results for burnout prediction and allows a significant decrease of undesirable hospitalizations when used as decision aid for admission control.