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Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.
Masino, Aaron J; Harris, Mary Catherine; Forsyth, Daniel; Ostapenko, Svetlana; Srinivasan, Lakshmi; Bonafide, Christopher P; Balamuth, Fran; Schmatz, Melissa; Grundmeier, Robert W.
Afiliación
  • Masino AJ; Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
  • Harris MC; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
  • Forsyth D; Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
  • Ostapenko S; Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, United States of America.
  • Srinivasan L; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
  • Bonafide CP; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
  • Balamuth F; Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
  • Schmatz M; Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, United States of America.
  • Grundmeier RW; Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
PLoS One ; 14(2): e0212665, 2019.
Article en En | MEDLINE | ID: mdl-30794638
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Diagnóstico por Computador / Sepsis / Cuidados Críticos / Registros Electrónicos de Salud / Aprendizaje Automático / Modelos Biológicos Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Screening_studies Límite: Female / Humans / Infant / Male / Newborn Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Diagnóstico por Computador / Sepsis / Cuidados Críticos / Registros Electrónicos de Salud / Aprendizaje Automático / Modelos Biológicos Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Screening_studies Límite: Female / Humans / Infant / Male / Newborn Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos