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Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.
Wang, Yanshan; Zhao, Yiqing; Therneau, Terry M; Atkinson, Elizabeth J; Tafti, Ahmad P; Zhang, Nan; Amin, Shreyasee; Limper, Andrew H; Khosla, Sundeep; Liu, Hongfang.
Afiliación
  • Wang Y; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. Electronic address: Wang.Yanshan@mayo.edu.
  • Zhao Y; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Therneau TM; Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Atkinson EJ; Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Tafti AP; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Zhang N; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Amin S; Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
  • Limper AH; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
  • Khosla S; Division of Endocrinology and Kogod Center on Aging, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
  • Liu H; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. Electronic address: Liu.Hongfang@mayo.edu.
J Biomed Inform ; 102: 103364, 2020 02.
Article en En | MEDLINE | ID: mdl-31891765
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Registros Electrónicos de Salud / Aprendizaje Automático no Supervisado Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Biomed Inform Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Registros Electrónicos de Salud / Aprendizaje Automático no Supervisado Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Biomed Inform Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article Pais de publicación: Estados Unidos