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Prediction of ROSC After Cardiac Arrest Using Machine Learning
Nan Liu, Andrew Fu Wah Ho, Pin Pin Pek, Tsung-Chien Lu, Pairoj Khruekarnchana, Kyoung Jun Song, Hideharu Tanaka, Ghulam Yasin Naroo, Han Nee Gan, Zhi Xiong Koh, Huei-Ming Ma, Marcus Ong
1357 - 1358
10.3233/SHTI200440
Research Article
Studies in Health Technology and Informatics
Volume 270: Digital Personalized Health and Medicine
Abstract

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.

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