Your browser doesn't support javascript.
loading
Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study.
Ikemura, Kenji; Bellin, Eran; Yagi, Yukako; Billett, Henny; Saada, Mahmoud; Simone, Katelyn; Stahl, Lindsay; Szymanski, James; Goldstein, D Y; Reyes Gil, Morayma.
Afiliación
  • Ikemura K; Department of Pathology, Albert Einstein College of Medicine, Montefiore Medical Center, The Bronx, NY, United States.
  • Bellin E; Tsubomi Technology, The Bronx, NY, United States.
  • Yagi Y; Department of Epidemiology and Population Health and Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, The Bronx, NY, United States.
  • Billett H; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
  • Saada M; Department of Oncology and Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, The Bronx, NY, United States.
  • Simone K; Tsubomi Technology, The Bronx, NY, United States.
  • Stahl L; Tsubomi Technology, The Bronx, NY, United States.
  • Szymanski J; Department of Epidemiology and Population Health and Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, The Bronx, NY, United States.
  • Goldstein DY; Department of Pathology, Albert Einstein College of Medicine, Montefiore Medical Center, The Bronx, NY, United States.
  • Reyes Gil M; Department of Pathology, Albert Einstein College of Medicine, Montefiore Medical Center, The Bronx, NY, United States.
J Med Internet Res ; 23(2): e23458, 2021 02 26.
Article en En | MEDLINE | ID: mdl-33539308
Asunto(s)
Palabras clave
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Aprendizaje Automático / COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: J Med Internet Res Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Aprendizaje Automático / COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: J Med Internet Res Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos