Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.

Guo, Lin Lawrence; Pfohl, Stephen R; Fries, Jason; Posada, Jose; Fleming, Scott Lanyon; Aftandilian, Catherine; Shah, Nigam; Sung, Lillian

Guo, Lin Lawrence; Pfohl, Stephen R; Fries, Jason; Posada, Jose; Fleming, Scott Lanyon; Aftandilian, Catherine; Shah, Nigam; Sung, Lillian.

Afiliación

Guo LL; Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
Pfohl SR; Biomedical Informatics Research, Stanford University, Palo Alto, California, United States.
Fries J; Biomedical Informatics Research, Stanford University, Palo Alto, California, United States.
Posada J; Biomedical Informatics Research, Stanford University, Palo Alto, California, United States.
Fleming SL; Biomedical Informatics Research, Stanford University, Palo Alto, California, United States.
Aftandilian C; Division of Pediatric Hematology/Oncology, Stanford University, Palo Alto, United States.
Shah N; Biomedical Informatics Research, Stanford University, Palo Alto, California, United States.
Sung L; Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.

Appl Clin Inform ; 12(4): 808-815, 2021 08.

Article en En | MEDLINE | ID: mdl-34470057

Asunto(s)

Medicina Clínica; Aprendizaje Automático; Toma de Decisiones Clínicas; Cognición

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Medicina Clínica / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Systematic_reviews Idioma: En Revista: Appl Clin Inform Año: 2021 Tipo del documento: Article País de afiliación: Canadá

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google