Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель прогнозирования повторной госпитализации× | Эффективность больниц по методу DEA× | |
|---|---|---|
| Область | Управление здравоохранением | Управление здравоохранением |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1998 | 1978 |
| Автор метода≠ | Healthcare data analytics and outcomes research | Abraham Charnes, William Cooper, Edward Rhodes |
| Тип≠ | Logistic regression and machine learning methodology | Non-parametric frontier estimation technique |
| Основополагающий источник≠ | 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 ↗ | Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. DOI ↗ |
| Другие названия | Readmission Risk Prediction, Hospital Readmission Forecasting | Hospital DEA, Healthcare DEA |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Data Envelopment Analysis (DEA) is a linear programming technique for measuring the relative efficiency of multiple hospitals using multiple inputs and outputs. Introduced by Charnes, Cooper, and Rhodes in 1978, DEA has become the standard method for benchmarking hospital performance in healthcare systems worldwide. |
| ScholarGateНабор данных ↗ |
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