Lee YJ, Cho KJ, Kwon O, et al. - This study was attempted to confirm deep learning (DL)-based early warning score (DEWS) in multiple centres and compare the prediction, alarming, and timeliness performance with the modified early warning score (MEWS) to distinguish patients at risk for in-hospital cardiac arrest (IHCA). Researchers conducted a retrospective cohort study including adult patients admitted to the general wards of five hospitals during a 12-month period. The discrimination was evaluated using the area under the receiver operating characteristic curve (AUROC). In this study, a total of 173,368 patients were included (224 IHCAs). The outcomes exhibited that DEWS was superior to MEWS in three key aspects (IHCA predictive, alarming, and timeliness performance). The findings revealed the potential of DEWS as an effective, efficient screening tool in rapid response systems to distinguish high-risk patients.