Objective: We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality. Materials and Methods: Data were from a health system's EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12-to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches. Results: Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP). Conclusion: Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12-18 months. Key words: heart failure prediction, deep learning, recurrent neural network, patient progression model, electronic health records OBJECTIVE Before diagnosis of a disease, an individual's progression mediated by pathophysiologic changes distinguishes those who will eventually get the disease from those who will not. Detection of temporal event sequences that reliably distinguish disease cases from controls may be particularly useful in improving predictive model performance. We investigated whether recurrent neural network (RNN) models could be adapted for this purpose, converting clinical event sequences and related time-stamped data into pathways relevant to early detection of disease. Electronic health record (EHR) data capture rich clinical and related temporal information. Patient health care encounters are well documented (eg, diagnoses, medications, and procedures) and timestamped. However, EHR data are highly complex, given the structure and breadth of information captured (spanning provider behavior, care utilization, treatment pathways, and patient disease state) and irregular sampling frequency. To date, most predictive modeling work using EHR data rely on aggregate features (eg, event count and event average). Temporal relations among disaggregated features (eg, medication ordered at one time and procedure performed at another) are not captured using these methods. We applied RNN models to heart failure (HF) cases and controls using longitudinal EHR data, and compared the model performance to traditional machine learning approaches. HF is one of the leading  BACKGROUND AND SIGNIFICANCE
Choi et al. REF explored recurrent neural network whether improving initial diagnosis of heart failure compared to traditional machine learning-based approaches.
Using recurrent neural network models for early detection of heart failure onset
Journal of the American Medical Informatics Association
Medicine Computer Science