Sepsis Prediction for the General Ward Setting.

Yu, Sean C; Gupta, Aditi; Betthauser, Kevin D; Lyons, Patrick G; Lai, Albert M; Kollef, Marin H; Payne, Philip R O; Michelson, Andrew P

Yu, Sean C; Gupta, Aditi; Betthauser, Kevin D; Lyons, Patrick G; Lai, Albert M; Kollef, Marin H; Payne, Philip R O; Michelson, Andrew P.

Afiliación

Yu SC; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO, United States.
Gupta A; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States.
Betthauser KD; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO, United States.
Lyons PG; Department of Pharmacy, Barnes-Jewish Hospital, St. Louis, MO, United States.
Lai AM; Division of Pulmonary and Critical Care, Washington University School of Medicine in St. Louis, St. Louis, MO, United States.
Kollef MH; Healthcare Innovation Lab, BJC HealthCare, and Washington University in St. Louis School of Medicine, St. Louis, MO, United States.
Payne PRO; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO, United States.
Michelson AP; Division of Pulmonary and Critical Care, Washington University School of Medicine in St. Louis, St. Louis, MO, United States.

Front Digit Health ; 4: 848599, 2022.

Article en En | MEDLINE | ID: mdl-35350226

Palabras clave

electronic health records; general ward; machine learning; prediction; sepsis

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Digit Health Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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