Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study.

Lee, Jewel Y; Molani, Sevda; Fang, Chen; Jade, Kathleen; O'Mahony, D Shane; Kornilov, Sergey A; Mico, Lindsay T; Hadlock, Jennifer J

Lee, Jewel Y; Molani, Sevda; Fang, Chen; Jade, Kathleen; O'Mahony, D Shane; Kornilov, Sergey A; Mico, Lindsay T; Hadlock, Jennifer J.

Afiliación

Lee JY; Institute for Systems Biology, Seattle, WA, United States.
Molani S; Institute for Systems Biology, Seattle, WA, United States.
Fang C; Institute for Systems Biology, Seattle, WA, United States.
Jade K; Institute for Systems Biology, Seattle, WA, United States.
O'Mahony DS; Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA, United States.
Kornilov SA; Institute for Systems Biology, Seattle, WA, United States.
Mico LT; Providence St Joseph Health, Renton, WA, United States.
Hadlock JJ; Institute for Systems Biology, Seattle, WA, United States.

JMIR Med Inform ; 9(7): e29986, 2021 Jul 08.

Article en En | MEDLINE | ID: mdl-34086596

Palabras clave

clinical decision making; electronic health records; machine learning; prevention; risk factors; risk prediction; sepsis

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: JMIR Med Inform Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

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