Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction.

Mohamadlou, Hamid; Panchavati, Saarang; Calvert, Jacob; Lynn-Palevsky, Anna; Le, Sidney; Allen, Angier; Pellegrini, Emily; Green-Saxena, Abigail; Barton, Christopher; Fletcher, Grant; Shieh, Lisa; Stark, Philip B; Chettipally, Uli; Shimabukuro, David; Feldman, Mitchell; Das, Ritankar

Mohamadlou, Hamid; Panchavati, Saarang; Calvert, Jacob; Lynn-Palevsky, Anna; Le, Sidney; Allen, Angier; Pellegrini, Emily; Green-Saxena, Abigail; Barton, Christopher; Fletcher, Grant; Shieh, Lisa; Stark, Philip B; Chettipally, Uli; Shimabukuro, David; Feldman, Mitchell; Das, Ritankar.

Afiliación

Green-Saxena A; Dascena, Inc., USA.
Barton C; University of California San Francisco, USA.
Fletcher G; University of Washington, USA.
Shieh L; Stanford University, USA.
Stark PB; University of California, Berkeley, USA.
Chettipally U; University of California San Francisco, USA; Kaiser Permanente South San Francisco Medical Center, USA.
Feldman M; University of California San Francisco, USA.
Das R; Dascena, Inc., USA.

Health Informatics J ; 26(3): 1912-1925, 2020 09.

Article en En | MEDLINE | ID: mdl-31884847

Asunto(s)

Aprendizaje Automático; Sepsis; Algoritmos; Mortalidad Hospitalaria; Humanos; Estudios Retrospectivos

Palabras clave

electronic health record; machine learning; mortality; prediction

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis / Aprendizaje Automático Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Health Informatics J Año: 2020 Tipo del documento: Article Pais de publicación: Reino Unido

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