Seoul researchers develop ML model to predict ICU cardiac arrest using ECG data alone

Early prediction of cardiac arrest could prevent deaths and improve survival rates for ICU patients experiencing this life-threatening event.

In the intensive care unit, where every second narrows the distance between life and death, researchers at Seoul National University Hospital have trained a machine learning model to read the heart's electrical whispers before they become a final silence. Using only the ECG data already flowing from bedside monitors, the system detects subtle fluctuations in heartbeat intervals — patterns invisible to human perception but legible to algorithm — to anticipate cardiac arrest before it strikes. The approach is notable not only for its predictive power but for its deliberate simplicity, requiring

Seoul researchers develop ML model to predict ICU cardiac arrest using ECG data alone

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