Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units
This work addresses predicting cardiovascular diseases for patients using EHR data, showing incremental improvements in accuracy for short time horizons.
The paper tackled predicting cardiovascular events from electronic health records using a multi-task recurrent neural network with attention, achieving AUCs of 0.85 for stroke and 0.89 for myocardial infarction, outperforming standard clinical risk scores.
In this work, we propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records (EHRs) at different time horizons. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust. The proposed model outperforms standard clinical risk scores in predicting stroke (AUC=0.85) and myocardial infarction (AUC=0.89), considering the largest time horizon. Benefit of using an \gls{mt} setting becomes visible for very short time horizons, which results in an AUC increase between 2-6%. Further, we explored the importance of individual features and attention weights in predicting cardiovascular events. Our results indicate that the recurrent neural network approach benefits from the hospital longitudinal information and demonstrates how machine learning techniques can be applied to secondary care.