A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure.

Luo, Cida; Zhu, Yi; Zhu, Zhou; Li, Ranxi; Chen, Guoqin; Wang, Zhang

Luo, Cida; Zhu, Yi; Zhu, Zhou; Li, Ranxi; Chen, Guoqin; Wang, Zhang.

Afiliação

Luo C; South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China.
Zhu Y; School of Life Sciences, South China Normal University, Guangzhou, 510631, Guangdong, China.
Zhu Z; Department of Cardiology, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China.
Li R; South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China.
Chen G; School of Life Sciences, South China Normal University, Guangzhou, 510631, Guangdong, China.
Wang Z; South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China.

J Transl Med ; 20(1): 136, 2022 03 18.

Article em En | MEDLINE | ID: mdl-35303896

Assuntos

Cuidados Críticos; Insuficiência Cardíaca; Mortalidade Hospitalar; Humanos; Unidades de Terapia Intensiva; Aprendizado de Máquina; Medição de Risco

Palavras-chave

Extreme gradient boosting; Heart failure; Machine learning models; Medical information mart for intensive care; Risk stratification

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cuidados Críticos / Insuficiência Cardíaca Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Transl Med Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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