Time-to-event modeling for hospital length of stay prediction for COVID-19 patients.

Wen, Yuxin; Rahman, Md Fashiar; Zhuang, Yan; Pokojovy, Michael; Xu, Honglun; McCaffrey, Peter; Vo, Alexander; Walser, Eric; Moen, Scott; Tseng, Tzu-Liang Bill

Wen, Yuxin; Rahman, Md Fashiar; Zhuang, Yan; Pokojovy, Michael; Xu, Honglun; McCaffrey, Peter; Vo, Alexander; Walser, Eric; Moen, Scott; Tseng, Tzu-Liang Bill.

Afiliação

Wen Y; Dale E. and Sarah Ann Fowler School of Engineering, Chapman University, Orange, CA 92866, USA.
Rahman MF; Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA.
Zhuang Y; Department of Biomedical Engineering, Sichuan University, Chengdu, Sichuan 610065, China.
Pokojovy M; Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA.
Xu H; Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA.
McCaffrey P; The University of Texas Medical Branch, Galveston, TX 77550, USA.
Vo A; The University of Texas Medical Branch, Galveston, TX 77550, USA.
Walser E; The University of Texas Medical Branch, Galveston, TX 77550, USA.
Moen S; The University of Texas Medical Branch, Galveston, TX 77550, USA.
Tseng TB; Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA.

Mach Learn Appl ; 9: 100365, 2022 Sep 15.

Article em En | MEDLINE | ID: mdl-35756359

Palavras-chave

COVID-19; Deep learning; Length of stay; Survival analysis; Time-to-event modeling

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Mach Learn Appl Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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