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Machine Learning Approaches to Identify Factors Associated with Women's Vasomotor Symptoms Using General Hospital Data.
Ryu, Ki Jin; Yi, Kyong Wook; Kim, Yong Jin; Shin, Jung Ho; Hur, Jun Young; Kim, Tak; Seo, Jong Bae; Lee, Kwang Sig; Park, Hyuntae.
  • Ryu KJ; Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
  • Yi KW; Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
  • Kim YJ; Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
  • Shin JH; Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
  • Hur JY; Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
  • Kim T; Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Korea.
  • Seo JB; Department of Biosciences, Mokpo National University, Muan, Korea.
  • Lee KS; Department of Biomedicine, Health & Life Convergence Science, Mokpo National University, Muan, Korea.
  • Park H; AI Center, Korea University College of Medicine, Seoul, Korea. ecophy@hanmail.net.
J Korean Med Sci ; 36(17): e122, 2021 May 03.
Article en En | MEDLINE | ID: mdl-33942581
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Sistema Vasomotor / Peso Corporal / Menopausia / Salud de la Mujer / Sofocos / Aprendizaje Automático Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Middle aged País como asunto: Asia Idioma: En Año: 2021 Tipo del documento: Article
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Sistema Vasomotor / Peso Corporal / Menopausia / Salud de la Mujer / Sofocos / Aprendizaje Automático Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Middle aged País como asunto: Asia Idioma: En Año: 2021 Tipo del documento: Article