Liu, NanNanLiuHo, Andrew Fu WahAndrew Fu WahHoPek, Pin PinPin PinPekTSUNG-CHIEN LUKhruekarnchana, PairojPairojKhruekarnchanaSong, Kyoung JunKyoung JunSongTanaka, HideharuHideharuTanakaNaroo, Ghulam YasinGhulam YasinNarooGan, Han NeeHan NeeGanKoh, Zhi XiongZhi XiongKohMATTHEW HUEI-MING MAOng, MarcusMarcusOng2020-12-292020-12-292020-06-16https://scholars.lib.ntu.edu.tw/handle/123456789/535580Out-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.enOut-of-hospital cardiac arrest; ROSC; machine learning; random forest[SDGs]SDG3Machine learning; Medical informatics; Medicine; Resuscitation; Cardiac arrest; Machine learning models; Predictive models; Survival rate; Predictive analytics; emergency health service; human; machine learning; out of hospital cardiac arrest; physiological process; resuscitation; retrospective study; survival rate; Cardiopulmonary Resuscitation; Emergency Medical Services; Humans; Machine Learning; Out-of-Hospital Cardiac Arrest; Physiological Phenomena; Retrospective Studies; Survival RatePrediction of ROSC After Cardiac Arrest Using Machine Learningconference paper10.3233/SHTI20044032570657