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Development and Validation of a Machine Learning Model to Identify Patients Before Surgery at High Risk for Postoperative Adverse Events.
Mahajan, Aman; Esper, Stephen; Oo, Thien Htay; McKibben, Jeffery; Garver, Michael; Artman, Jamie; Klahre, Cynthia; Ryan, John; Sadhasivam, Senthilkumar; Holder-Murray, Jennifer; Marroquin, Oscar C.
Afiliação
  • Mahajan A; Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Esper S; Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Oo TH; Department of Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • McKibben J; Department of Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Garver M; Department of Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Artman J; Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Klahre C; Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Ryan J; Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Sadhasivam S; Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Holder-Murray J; Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Marroquin OC; Department of Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
JAMA Netw Open ; 6(7): e2322285, 2023 Jul 03.
Article em En | MEDLINE | ID: mdl-37418262
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Complicações Pós-Operatórias / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Middle aged Idioma: En Revista: JAMA Netw Open Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Complicações Pós-Operatórias / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Middle aged Idioma: En Revista: JAMA Netw Open Ano de publicação: 2023 Tipo de documento: Article