Machine learning models for predicting in-hospital mortality in patient with sepsis: Analysis of vital sign dynamics.

Cheng, Chi-Yung; Kung, Chia-Te; Chen, Fu-Cheng; Chiu, I-Min; Lin, Chun-Hung Richard; Chu, Chun-Chieh; Kung, Chien Feng; Su, Chih-Min

Cheng, Chi-Yung; Kung, Chia-Te; Chen, Fu-Cheng; Chiu, I-Min; Lin, Chun-Hung Richard; Chu, Chun-Chieh; Kung, Chien Feng; Su, Chih-Min.

Afiliação

Cheng CY; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
Kung CT; Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Chen FC; Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Chiu IM; Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Lin CR; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
Chu CC; Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Kung CF; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
Su CM; Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.

Front Med (Lausanne) ; 9: 964667, 2022.

Article em En | MEDLINE | ID: mdl-36341257

Palavras-chave

dynamic vital sign; machine learning model; mortality; prediction model; sepsis

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Med (Lausanne) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Taiwan

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