Salīdzināt metodes

	Modelis slimnīcas atkārtotas uzņemšanas prognozēšanai	Modelis slimnīcas gultu noslogojumam
Nozare	Veselības aprūpes vadība	Veselības aprūpes vadība
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1998	2000
Autors	Healthcare data analytics and outcomes research	Healthcare operations researchers
Tips	Logistic regression and machine learning methodology	Stochastic simulation and time-series forecasting
Pirmavots	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.
Citi nosaukumi	Readmission Risk Prediction, Hospital Readmission Forecasting	Bed Occupancy Forecasting, Hospital Census Prediction
Saistītās	5	5
Kopsavilkums	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.