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Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach.
Desautels, Thomas; Calvert, Jacob; Hoffman, Jana; Jay, Melissa; Kerem, Yaniv; Shieh, Lisa; Shimabukuro, David; Chettipally, Uli; Feldman, Mitchell D; Barton, Chris; Wales, David J; Das, Ritankar.
Afiliação
  • Desautels T; Dascena, Inc, Hayward, CA, United States.
  • Calvert J; Dascena, Inc, Hayward, CA, United States.
  • Hoffman J; Dascena, Inc, Hayward, CA, United States.
  • Jay M; Dascena, Inc, Hayward, CA, United States.
  • Kerem Y; Department of Clinical Informatics, Stanford University School of Medicine, Stanford, CA, United States
  • Shieh L; Department of Emergency Medicine, Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
  • Shimabukuro D; Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
  • Chettipally U; Division of Critical Care Medicine, Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, United States
  • Feldman MD; Department of Emergency Medicine, Kaiser Permanente South San Francisco Medical Center, South San Francisco, CA, United States
  • Barton C; Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, United States
  • Wales DJ; Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
  • Das R; Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, United States
JMIR Med Inform ; 4(3): e28, 2016 Sep 30.
Article em En | MEDLINE | ID: mdl-27694098
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article