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In this work, we propose a multi-task recurrent neural network with attentionmechanism for predicting cardiovascular events from electronic health records(EHRs) at different time horizons. The proposed approach is compared to astandard clinical risk predictor (QRISK) and machine learning alternativesusing 5-year data from a NHS Foundation Trust. The proposed model outperformsstandard clinical risk scores in predicting stroke (AUC=0.85) and myocardialinfarction (AUC=0.89), considering the largest time horizon. Benefit of usingan \gls{mt} setting becomes visible for very short time horizons, which resultsin an AUC increase between 2-6%. Further, we explored the importance ofindividual features and attention weights in predicting cardiovascular events.Our results indicate that the recurrent neural network approach benefits fromthe hospital longitudinal information and demonstrates how machine learningtechniques can be applied to secondary care.

Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units