Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель прогнозирования повторной госпитализации× | Имитационное моделирование потоков пациентов× | |
|---|---|---|
| Область | Управление здравоохранением | Управление здравоохранением |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1998 | 1990 |
| Автор метода≠ | Healthcare data analytics and outcomes research | Operations research and management science |
| Тип≠ | Logistic regression and machine learning methodology | Discrete event simulation 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 ↗ | Pidd, M. (1992). Computer Simulation in Management Science (3rd ed.). John Wiley & Sons. ISBN: 9780471939314 |
| Другие названия | Readmission Risk Prediction, Hospital Readmission Forecasting | Healthcare DES, Patient Movement Simulation |
| 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. | Discrete Event Simulation (DES) is a computational technique that models the movement of patients through healthcare facilities by simulating individual patient journeys and interactions with resources (staff, beds, equipment). DES allows realistic representation of complex, stochastic healthcare processes and supports 'what-if' analysis without disrupting live operations. |
| ScholarGateНабор данных ↗ |
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