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Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.
Hara, Konan; Kobayashi, Yasuki; Tomio, Jun; Ito, Yuki; Svensson, Thomas; Ikesu, Ryo; Chung, Ung-Il; Svensson, Akiko Kishi.
Afiliação
  • Hara K; Department of Public Health, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
  • Kobayashi Y; Department of Public Health, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
  • Tomio J; Department of Public Health, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
  • Ito Y; Department of Economics, University of California, Berkeley, Berkeley, California, United States of America.
  • Svensson T; Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
  • Ikesu R; Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden.
  • Chung UI; School of Health Innovation, Kanagawa University of Human Services, Kawasaki-shi, Kanagawa, Japan.
  • Svensson AK; Department of Public Health, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
PLoS One ; 16(9): e0254394, 2021.
Article em En | MEDLINE | ID: mdl-34570785
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Revisão da Utilização de Seguros / Algoritmos / Bases de Dados Factuais / Diabetes Mellitus / Dislipidemias / Aprendizado de Máquina / Hipertensão Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male / Middle aged Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Japão
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Revisão da Utilização de Seguros / Algoritmos / Bases de Dados Factuais / Diabetes Mellitus / Dislipidemias / Aprendizado de Máquina / Hipertensão Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male / Middle aged Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Japão