Predicting heart failure in-hospital mortality by integrating longitudinal and category data in electronic health records

Heart failure is a life-threatening syndrome that is diagnosed in 3.6 million people worldwide each year. We propose a deep fusion learning model (DFL-IMP) that uses time series and category data from electronic health records to predict in-hospital mortality in patients with heart failure. We considered 41 time series features (platelets, white blood cells, urea nitrogen, etc.) and 17 category features (gender, insurance, marital status, etc.) as predictors, all of which were available within the time of the patient's last hospitalization, and a total of 7696 patients participated in the observational study. Our model was evaluated against different time windows. The best performance was achieved with an AUC of 0.914 when the observation window was 5 days and the prediction window was 30 days. Outperformed other baseline models including LR (0.708), RF (0.717), SVM (0.675), LSTM (0.757), GRU (0.759), GRU-U (0.766) and MTSSP (0.770). This tool allows us to predict the expected pathway of heart failure patients and intervene early in the treatment process, which has significant implications for improving the life expectancy of heart failure patients.

Automated summary

We propose a deep fusion learning model (DFL-IMP) that utilizes time series and category data from electronic health records to predict in-hospital mortality in patients with heart failure. By considering 41 time series features and 17 category features as predictors, our model achieved the best performance with an AUC of 0.914 when the observation window was 5 days and the prediction window was 30 days. Compared to other baseline models, the DFL-IMP model outperformed them significantly. This tool allows for predicting the expected pathway of heart failure patients and intervening early in the treatment process, thus improving their life expectancy significantly.