Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care.

Wang, Lin; Duan, Shao-Bin; Yan, Ping; Luo, Xiao-Qin; Zhang, Ning-Ya

Wang, Lin; Duan, Shao-Bin; Yan, Ping; Luo, Xiao-Qin; Zhang, Ning-Ya.

Afiliación

Wang L; Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Duan SB; Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Yan P; Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Luo XQ; Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Zhang NY; Information Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.

Ren Fail ; 45(1): 2215329, 2023 Dec.

Article en En | MEDLINE | ID: mdl-37218683

Asunto(s)

Cuidados Críticos; Unidades de Cuidados Intensivos; Anciano; Humanos; Riñón; Albúminas; Aprendizaje Automático

Palabras clave

Critical illness; XGBoost; machine learning; major adverse kidney events; the elderly

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cuidados Críticos / Unidades de Cuidados Intensivos Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Humans Idioma: En Revista: Ren Fail Asunto de la revista: NEFROLOGIA Año: 2023 Tipo del documento: Article País de afiliación: China

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