Prediction of ROSC After Cardiac Arrest Using Machine Learning.

Autor:	Liu N; Health Services Research Centre, Singapore Health Services, Singapore.; Health Services and Systems Research, Duke-NUS Medical School, Singapore., Ho AFW; Health Services and Systems Research, Duke-NUS Medical School, Singapore.; Department of Emergency Medicine, Singapore General Hospital, Singapore., Pek PP; Health Services and Systems Research, Duke-NUS Medical School, Singapore.; Department of Emergency Medicine, Singapore General Hospital, Singapore., Lu TC; Department of Emergency Medicine, National Taiwan University Hospital, Taiwan., Khruekarnchana P; Department of Emergency Medicine, Rajavithi Hospital, Thailand., Song KJ; Seoul National University College of Medicine, Republic of Korea., Tanaka H; Graduate School of EMS System, Kokushikan University, Japan., Naroo GY; ED-Trauma Centre, Rashid Hospital, Dubai, United Arab Emirates., Gan HN; Accident & Emergency Department, Changi General Hospital, Singapore., Koh ZX; Department of Emergency Medicine, Singapore General Hospital, Singapore., Ma HM; Department of Emergency Medicine, National Taiwan University Hospital, Taiwan., Ong M; Health Services and Systems Research, Duke-NUS Medical School, Singapore.; Department of Emergency Medicine, Singapore General Hospital, Singapore.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2020 Jun 16; Vol. 270, pp. 1357-1358.
DOI:	10.3233/SHTI200440
Abstrakt:	Out-of-hospital cardiac arrest (OHCA) is an important public health problem, with very low survival rate. In treating OHCA patients, the return of spontaneous circulation (ROSC) represents the success of early resuscitation efforts. In this study, we developed a machine learning model to predict ROSC and compared it with the ROSC after cardiac arrest (RACA) score. Results demonstrated the usefulness of machine learning in deriving predictive models.
Databáze:	MEDLINE
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