De facto diagnosis specialties: Recognition and discovery.

Zhang, Aston; Lu, Xun; Gunter, Carl A; Yao, Shuochao; Tao, Fangbo; Zhu, Rongda; Gui, Huan; Fabbri, Daniel; Liebovitz, David; Malin, Bradley

Zhang, Aston; Lu, Xun; Gunter, Carl A; Yao, Shuochao; Tao, Fangbo; Zhu, Rongda; Gui, Huan; Fabbri, Daniel; Liebovitz, David; Malin, Bradley.

Afiliación

Zhang A; Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois.
Lu X; Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois.
Gunter CA; Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois.
Yao S; Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois.
Tao F; Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois.
Zhu R; Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois.
Gui H; Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois.
Fabbri D; Department of Biomedical Informatics Vanderbilt University Nashville Tennessee.
Liebovitz D; Department of Medicine University of Chicago Chicago Illinois.
Malin B; Department of Biomedical Informatics Vanderbilt University Nashville Tennessee.

Learn Health Syst ; 2(3): e10057, 2018 Jul.

Article en En | MEDLINE | ID: mdl-31245585

Palabras clave

diagnosis specialty; electronic health record; machine learning; medical informatics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Learn Health Syst Año: 2018 Tipo del documento: Article Pais de publicación: Estados Unidos

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