Endpoint prediction of heart failure using electronic health records.

Abstract

Heart failure (HF) is a serious condition associated with high morbidity and mortality rates. Effective endpoint prediction in patient treatment trajectories provides preventative information about HF prognosis, guides decision making about the type and intensity of care, and enables better understanding of provider performance. We explored the potential of a large volume of electronic health records (EHRs) for endpoint prediction of HF. Specifically, a suite of patient features observed at the prediction time point were utilized as the auxiliary information during the training of the prediction model. We extract the latent representation of patient treatment trajectory by equipping a recurrent neural network (RNN) with two learning strategies, namely adversarial learning and multi-task learning. As for the adversarial learning strategy, an adversarial learning scheme is used to differentiate the generated feature vector from the real one, while in the multi-task learning strategy, we consider the prediction of patient feature vector as an auxiliary task other than endpoint prediction. With such learning strategies, the extracted representation of patient treatment trajectory is particularly optimized for predicting HF endpoint, including HF-readmission, all-cause mortality and their combination (i.e., composite endpoint). We evaluate the proposed approach on a real clinical dataset collected from a Chinese hospital. The experimental dataset contains 2102 HF patient treatment trajectories with 13,545 visits on the hospital. The area under the ROC curve (AUC) achieved by our best model in predicting composite endpoint is 0.744, which is better than that of state-of-the-art models, including the standard Long Short Term Memory (0.727), Gated Recurrent Unit (0.732), RETAIN(0.730). With respect to the prediction of HF-readmission and all-cause mortality, our method also shows better performance than benchmark models. The experimental results show that the proposed model can achieve competitive performance over state-of-the-art models in terms of endpoint prediction for HF, and reveal some suggestive hypotheses that could be validated by further investigations in the medical domain.