A prediction and interpretation framework of acute kidney injury in critical care.

Gong, Kaidi; Lee, Hyo Kyung; Yu, Kaiye; Xie, Xiaolei; Li, Jingshan

Gong, Kaidi; Lee, Hyo Kyung; Yu, Kaiye; Xie, Xiaolei; Li, Jingshan.

Afiliação

Gong K; Department of Industrial Engineering, Tsinghua University, Beijing 100084, China. Electronic address: gjd18@mails.tsinghua.edu.cn.
Lee HK; Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA. Electronic address: hkl14@pitt.edu.
Yu K; Department of Industrial Engineering, Tsinghua University, Beijing 100084, China. Electronic address: yky15@mails.tsinghua.edu.cn.
Xie X; Department of Industrial Engineering, Tsinghua University, Beijing 100084, China. Electronic address: xxie@tsinghua.edu.cn.
Li J; Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA. Electronic address: jingshan.li@wisc.edu.

J Biomed Inform ; 113: 103653, 2021 01.

Article em En | MEDLINE | ID: mdl-33338667

Assuntos

Injúria Renal Aguda; Injúria Renal Aguda/diagnóstico; Cuidados Críticos; Hospitalização; Humanos; Unidades de Terapia Intensiva; Aprendizado de Máquina

Palavras-chave

Acute kidney injury (AKI); Clinical decision support; Machine learning; Model interpretation; Prediction model

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Injúria Renal Aguda Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Biomed Inform Ano de publicação: 2021 Tipo de documento: Article

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