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| Модел за прогнозиране на повторни хоспитализации× | Модел за заетост на болнични легла× | |
|---|---|---|
| Област | Управление на здравеопазването | Управление на здравеопазването |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1998 | 2000 |
| Създател≠ | Healthcare data analytics and outcomes research | Healthcare operations researchers |
| Тип≠ | Logistic regression and machine learning methodology | Stochastic simulation and time-series forecasting |
| Основополагащ източник≠ | 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. DOI ↗ | 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. link ↗ |
| Други названия | Readmission Risk Prediction, Hospital Readmission Forecasting | Bed Occupancy Forecasting, Hospital Census Prediction |
| Свързани | 5 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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