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Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.
Ginestra, Jennifer C; Giannini, Heather M; Schweickert, William D; Meadows, Laurie; Lynch, Michael J; Pavan, Kimberly; Chivers, Corey J; Draugelis, Michael; Donnelly, Patrick J; Fuchs, Barry D; Umscheid, Craig A.
Afiliação
  • Ginestra JC; Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.
  • Giannini HM; Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.
  • Schweickert WD; Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
  • Meadows L; University of Pennsylvania Health System, Philadelphia, PA.
  • Lynch MJ; Department of Nursing, Hospital of the University of Pennsylvania, Philadelphia, PA.
  • Pavan K; Department of Nursing, Hospital of the University of Pennsylvania, Philadelphia, PA.
  • Chivers CJ; Department of Clinical Informatics, Penn Presbyterian Medical Center, Philadelphia, PA.
  • Draugelis M; University of Pennsylvania Health System, Philadelphia, PA.
  • Donnelly PJ; University of Pennsylvania Health System, Philadelphia, PA.
  • Fuchs BD; Pennsylvania Hospital, Philadelphia, PA.
  • Umscheid CA; Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
Crit Care Med ; 47(11): 1477-1484, 2019 11.
Article em En | MEDLINE | ID: mdl-31135500
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Choque Séptico / Algoritmos / Atitude do Pessoal de Saúde / Sepse / Sistemas de Apoio a Decisões Clínicas / Aprendizado de Máquina Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Choque Séptico / Algoritmos / Atitude do Pessoal de Saúde / Sepse / Sistemas de Apoio a Decisões Clínicas / Aprendizado de Máquina Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article