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Evaluation of Machine Learning Algorithms for Predicting Readmission After Acute Myocardial Infarction Using Routinely Collected Clinical Data.
Gupta, Shagun; Ko, Dennis T; Azizi, Paymon; Bouadjenek, Mohamed Reda; Koh, Maria; Chong, Alice; Austin, Peter C; Sanner, Scott.
Afiliação
  • Gupta S; Department of Mechanical and Industrial Engineering, University of Toronto, Ontario, Canada.
  • Ko DT; Schulich Heart Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada; Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada. Electronic ad
  • Azizi P; Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
  • Bouadjenek MR; Department of Mechanical and Industrial Engineering, University of Toronto, Ontario, Canada.
  • Koh M; Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada.
  • Chong A; Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada.
  • Austin PC; Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
  • Sanner S; Department of Mechanical and Industrial Engineering, University of Toronto, Ontario, Canada.
Can J Cardiol ; 36(6): 878-885, 2020 06.
Article em En | MEDLINE | ID: mdl-32204950
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male / Middle aged País/Região como assunto: America do norte 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: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male / Middle aged País/Região como assunto: America do norte Idioma: En Ano de publicação: 2020 Tipo de documento: Article