Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study.

Hong, Sungjun; Lee, Sungjoo; Lee, Jeonghoon; Cha, Won Chul; Kim, Kyunga

Hong, Sungjun; Lee, Sungjoo; Lee, Jeonghoon; Cha, Won Chul; Kim, Kyunga.

Afiliación

Hong S; Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
Lee S; Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
Lee J; Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
Cha WC; Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
Kim K; Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

JMIR Med Inform ; 8(8): e15932, 2020 Aug 04.

Article en En | MEDLINE | ID: mdl-32749227

Palabras clave

cardiac arrest prediction; clinical validity; emergency department; machine learning; sequential characteristics

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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: JMIR Med Inform Año: 2020 Tipo del documento: Article

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