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Application of machine learning to the prediction of postoperative sepsis after appendectomy.
Bunn, Corinne; Kulshrestha, Sujay; Boyda, Jason; Balasubramanian, Neelam; Birch, Steven; Karabayir, Ibrahim; Baker, Marshall; Luchette, Fred; Modave, François; Akbilgic, Oguz.
Afiliación
  • Bunn C; Department of Surgery, Loyola University Medical Center, Maywood, IL; Burn Shock Trauma Research Institute, Loyola University Chicago, Maywood, IL.
  • Kulshrestha S; Department of Surgery, Loyola University Medical Center, Maywood, IL; Burn Shock Trauma Research Institute, Loyola University Chicago, Maywood, IL.
  • Boyda J; Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood IL.
  • Balasubramanian N; Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood IL.
  • Birch S; Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood IL.
  • Karabayir I; Center for Health Outcomes and Informatics Research, Health Sciences Division, Loyola University Chicago, Maywood, IL; Department of Health Informatics and Data Science, Loyola University Chicago, Chicago, IL; Kirklareli University, Kirklareli, Turkey.
  • Baker M; Department of Surgery, Loyola University Medical Center, Maywood, IL; Edward Hines, Jr Veterans Administration Hospital, Hines, IL.
  • Luchette F; Department of Surgery, Loyola University Medical Center, Maywood, IL; Edward Hines, Jr Veterans Administration Hospital, Hines, IL.
  • Modave F; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL.
  • Akbilgic O; Center for Health Outcomes and Informatics Research, Health Sciences Division, Loyola University Chicago, Maywood, IL; Department of Health Informatics and Data Science, Loyola University Chicago, Chicago, IL. Electronic address: oakbilgic@luc.edu.
Surgery ; 169(3): 671-677, 2021 03.
Article en En | MEDLINE | ID: mdl-32951903
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Apendicectomía / Complicaciones Posoperatorias / Sepsis / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Año: 2021 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Apendicectomía / Complicaciones Posoperatorias / Sepsis / Aprendizaje Automático Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Año: 2021 Tipo del documento: Article