Development of a Transformer-Based Cardiac Arrest Prediction Model for General Ward Patients

Abstract Background In-hospital cardiac arrest on general wards is often preceded by detectable physiological deterioration, yet conventional early warning scores demonstrate limited discrimination. We developed and performed preliminary validation of a transformer-based cardiac arrest prediction system for general ward patients. Methods This retrospective study was conducted among general ward patients at a tertiary academic hospital in South Korea (Severance Hospital, 2013–2017). We developed Cardiac Arrest Risk Early Detection (CARED), a transformer-based system to predict 24-hour cardiac arrest risk. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Internal validation was performed using 5-fold cross-validation. Results In internal validation, CARED achieved the highest discrimination with an AUROC of 0.939 (95% CI: 0.928–0.950), significantly outperforming machine learning and deep learning baseline models (all P < 0.05) Conclusions CARED demonstrated high discriminative ability for predicting cardiac arrest in general ward patients. Comprehensive validations including multidimensional performance assessments, comparisons with conventional early warning scores, and external validation in independent populations are ongoing and will be reported in subsequent publications.