The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms.

Song, Jie; Gao, Yuan; Yin, Pengbin; Li, Yi; Li, Yang; Zhang, Jie; Su, Qingqing; Fu, Xiaojie; Pi, Hongying

Song, Jie; Gao, Yuan; Yin, Pengbin; Li, Yi; Li, Yang; Zhang, Jie; Su, Qingqing; Fu, Xiaojie; Pi, Hongying.

Afiliación

Song J; Medical School of Chinese PLA, Beijing, People's Republic of China.
Gao Y; First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China.
Yin P; Fouth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China.
Li Y; Medical School of Chinese PLA, Beijing, People's Republic of China.
Li Y; First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China.
Zhang J; Sixth Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China.
Su Q; Medical School of Chinese PLA, Beijing, People's Republic of China.
Fu X; First Medical Center, Chinese PLA General Hospital, Beijing, People's Republic of China.
Pi H; Medical Service Training Center, Chinese PLA General Hospital, Beijing, People's Republic of China.

Risk Manag Healthc Policy ; 14: 1175-1187, 2021.

Article en En | MEDLINE | ID: mdl-33776495

Palabras clave

adverse event; machine learning; pressure ulcer; risk management

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Risk Manag Healthc Policy Año: 2021 Tipo del documento: Article Pais de publicación: Reino Unido

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