Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.

Park, James Yeongjun; Hsu, Tzu-Chun; Hu, Jiun-Ruey; Chen, Chun-Yuan; Hsu, Wan-Ting; Lee, Matthew; Ho, Joshua; Lee, Chien-Chang

Park, James Yeongjun; Hsu, Tzu-Chun; Hu, Jiun-Ruey; Chen, Chun-Yuan; Hsu, Wan-Ting; Lee, Matthew; Ho, Joshua; Lee, Chien-Chang.

Afiliación

Park JY; Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States.
Hsu TC; Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Hu JR; Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.
Chen CY; Department of Medicine, National Taiwan University, Taipei, Taiwan.
Hsu WT; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States.
Lee M; Medical Wizdom, LLC, Brookline, MA, United States.
Ho J; Medical Wizdom, LLC, Brookline, MA, United States.
Lee CC; Center of Intelligent Healthcare, National Taiwan University Hospital, Taipei, Taiwan.

J Med Internet Res ; 24(4): e29982, 2022 04 13.

Article en En | MEDLINE | ID: mdl-35416785

Asunto(s)

Aprendizaje Automático; Sepsis; Adulto; Mortalidad Hospitalaria; Humanos; Modelos Logísticos; Curva ROC

Palabras clave

SuperLearner; machine learning; mortality; sepsis

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Sepsis / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adult / Humans Idioma: En Revista: J Med Internet Res Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google