Fig. 8: Schematic of prediction tasks, feature types and choice of learning algorithms.

a This study focused on developing and evaluating ECG based mortality models to predict the probability of a patient dying within 30-days, 1-year and 5-years, starting from the day of ECG acquisition. ECGs used in these models could have been acquired at any time point during a healthcare episode. Models included features with i ECG only, ii ECG + age, sex, and iii ECG + age, sex + lab tests. The goal of the prediction models is to output a calibrated probability of mortality, which could be used in patient risk-assessment. b Patient’s ECG data are generally archived by healthcare facilities as one of two formats: either i as a clinical report of summarised ECG measurements such as QT interval, QRS duration etc. or ii less commonly, as raw voltage time series of ECG signal tracings. In order to facilitate wider applicability, we used learning algorithms that are appropriate for the data formats, namely ResNet based deep learning for the information-rich multi-channel voltage time series and gradient boosting-based XGBoost for the ECG measurements.