Machine Learning Prediction of Kidney Stone Composition Using Electronic Health Record-Derived Features.

Abraham, Abin; Kavoussi, Nicholas L; Sui, Wilson; Bejan, Cosmin; Capra, John A; Hsi, Ryan

Abraham, Abin; Kavoussi, Nicholas L; Sui, Wilson; Bejan, Cosmin; Capra, John A; Hsi, Ryan.

Afiliación

Abraham A; Department of Biological Sciences, Vanderbilt Genetics Institute, and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, USA.
Kavoussi NL; Department of Urology and Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Sui W; Department of Urology and Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Bejan C; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Capra JA; Department of Biological Sciences, Vanderbilt Genetics Institute, and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, USA.
Hsi R; Bakar Computational Health Sciences Institute, University of California, San Francisco, California, USA.

J Endourol ; 36(2): 243-250, 2022 02.

Article en En | MEDLINE | ID: mdl-34314237

Asunto(s)

Registros Electrónicos de Salud; Cálculos Renales; Oxalato de Calcio; Humanos; Cálculos Renales/química; Aprendizaje Automático; Ácido Úrico

Palabras clave

24H urine; kidney stone; machine learning

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Cálculos Renales / Registros Electrónicos de Salud Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Endourol Asunto de la revista: UROLOGIA Año: 2022 Tipo del documento: Article

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