Deep learning predicts hip fracture using confounding patient and healthcare variables.

Badgeley, Marcus A; Zech, John R; Oakden-Rayner, Luke; Glicksberg, Benjamin S; Liu, Manway; Gale, William; McConnell, Michael V; Percha, Bethany; Snyder, Thomas M; Dudley, Joel T

Badgeley, Marcus A; Zech, John R; Oakden-Rayner, Luke; Glicksberg, Benjamin S; Liu, Manway; Gale, William; McConnell, Michael V; Percha, Bethany; Snyder, Thomas M; Dudley, Joel T.

Afiliación

Badgeley MA; Verily Life Sciences LLC, South San Francisco, CA USA.
Zech JR; 2Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA.
Oakden-Rayner L; 3Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA.
Glicksberg BS; 4Department of Medicine, California Pacific Medical Center, San Francisco, CA USA.
Liu M; 5School of Public Health, The University of Adelaide, Adelaide, South Australia Australia.
Gale W; 6Bakar Computational Health Sciences Institute, University of California, San Francisco, CA USA.
McConnell MV; Verily Life Sciences LLC, South San Francisco, CA USA.
Percha B; 7School of Computer Sciences, The University of Adelaide, Adelaide, South Australia Australia.
Snyder TM; Verily Life Sciences LLC, South San Francisco, CA USA.
Dudley JT; 8Division of Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA USA.

NPJ Digit Med ; 2: 31, 2019.

Article en En | MEDLINE | ID: mdl-31304378

Palabras clave

Computer science; Radiography; Statistics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: NPJ Digit Med Año: 2019 Tipo del documento: Article Pais de publicación: Reino Unido

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