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Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients.
Hasnain, Zaki; Mason, Jeremy; Gill, Karanvir; Miranda, Gus; Gill, Inderbir S; Kuhn, Peter; Newton, Paul K.
Afiliación
  • Hasnain Z; Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
  • Mason J; Department of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
  • Gill K; Department of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
  • Miranda G; USC Institute of Urology, Keck School of Medicine, University of Southern California, Health Sciences Campus, Los Angeles, CA, United States of America.
  • Gill IS; USC Institute of Urology, Keck School of Medicine, University of Southern California, Health Sciences Campus, Los Angeles, CA, United States of America.
  • Kuhn P; Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
  • Newton PK; Department of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.
PLoS One ; 14(2): e0210976, 2019.
Article en En | MEDLINE | ID: mdl-30785915
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Vejiga Urinaria / Cistectomía / Bases de Datos Factuales / Aprendizaje Automático / Modelos Biológicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Vejiga Urinaria / Cistectomía / Bases de Datos Factuales / Aprendizaje Automático / Modelos Biológicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos