Comparer des méthodes

	Modèle de prédiction de réadmission hospitalière	Modèles d'occupation des lits d'hôpital
Domaine	Gestion des soins de santé	Gestion des soins de santé
Famille	Process / pipeline	Process / pipeline
Année d'origine	1998	2000
Auteur d'origine	Healthcare data analytics and outcomes research	Healthcare operations researchers
Type	Logistic regression and machine learning methodology	Stochastic simulation and time-series forecasting
Source fondatrice	Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418–1428.	Tikk, D., Kóczy, L. T., & Gedeon, T. D. (2003). A survey on fuzzy relational equations and their applications in web intelligence. In W. Pedrycz (Ed.), Handbook of Granular Computing (pp. 521–542). John Wiley & Sons.
Alias	Readmission Risk Prediction, Hospital Readmission Forecasting	Bed Occupancy Forecasting, Hospital Census Prediction
Apparentées	5	5
Résumé	Hospital readmission prediction models use statistical and machine learning techniques to identify patients at high risk of returning to the hospital shortly after discharge. These models guide targeted discharge planning and follow-up to improve outcomes and reduce costs.	Hospital bed occupancy models forecast the number of occupied beds at future times by analyzing admission patterns, length of stay distributions, and discharge dynamics. These models support tactical decisions about staffing, supply chain management, and strategic decisions about capacity expansion.