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Identifying patterns and predictors of lifestyle modification in electronic health record documentation using statistical and machine learning methods.
Shoenbill, Kimberly; Song, Yiqiang; Craven, Mark; Johnson, Heather; Smith, Maureen; Mendonca, Eneida A.
Afiliação
  • Shoenbill K; Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA. Electronic address: kimberly_shoenbill@med.unc.edu.
  • Song Y; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
  • Craven M; Department of Biostatistics and Medical Informatics, Department of Computer Sciences, University of Wisconsin-Madison, 610 Walnut Street, 201 WARF, Madison, WI 53726, USA.
  • Johnson H; Department of Medicine, Division of Cardiovascular Medicine, University of Wisconsin-Madison, 5158 Medical Foundation Centennial Building, 1685 Highland Avenue, Madison, WI 53705, USA.
  • Smith M; Department of Population Health Sciences, Department of Family Medicine, University of Wisconsin-Madison, 800 University Bay Drive, Madison, WI 53705, USA.
  • Mendonca EA; Department of Biostatistics and Medical Informatics, Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA.
Prev Med ; 136: 106061, 2020 07.
Article em En | MEDLINE | ID: mdl-32179026
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article