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| 병원 재입원 예측 모델× | 병원 침상 점유율 모델× | |
|---|---|---|
| 분야 | 의료경영 | 의료경영 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1998 | 2000 |
| 창시자≠ | Healthcare data analytics and outcomes research | Healthcare operations researchers |
| 유형≠ | Logistic regression and machine learning methodology | Stochastic simulation and time-series forecasting |
| 원전≠ | 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 ↗ |
| 별칭 | Readmission Risk Prediction, Hospital Readmission Forecasting | Bed Occupancy Forecasting, Hospital Census Prediction |
| 관련 | 5 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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