Deep language model-based early recognition of out-of-hospital cardiac arrest from real-time emergency calls

We developed a dynamic deep learning model (DyLM-OHCA) for early out-of-hospital cardiac arrest (OHCA) detection. Using 158,973 emergency call transcripts from three South Korean metropolitan regions, we trained DyLM-OHCA for 60 s OHCA identification and compared its performance against four conventional machine learning algorithms-Logistic Regression, XGBoost, Gradient Boosting, and Random Forest. DyLM-OHCA markedly outperformed all other benchmarks (AUROC = 0.937; AUPRC = 0.456). We analyzed global and sample-level word importance and temporally predicted OHCA risk patterns. Word attribution revealed differences in important words between callers and dispatchers. OHCA recognition was influenced more by conversational flow than by individual keywords. True-positive cases sustained high-risk scores, whereas over half of false-positive cases showed early risk score decline. DyLM-OHCA captures clinically meaningful dialog patterns, moving beyond simple keyword spotting. By providing real-time, context-aware, and interpretable risk assessments, our model is potentially valuable in decision support, enhancing dispatcher confidence, and improving early OHCA recognition.