On Clinical Event Prediction in Patient Treatment Trajectory Using Longitudinal Electronic Health Records

Healthcare process leaves patient treatment trajectory (PTT), described as a sequence of interdependent clinical events affiliated with a large volume of longitudinal therapy and treatment information. Predicting the future clinical event in PTT, as a vital and essential task for providing insights into the entire treatment trajectory, can serve as an efficient and proactive altering service for health service delivery. However, it is challenging because there are long-term dependencies between clinical events, which are irregularly distributed along the temporal axis with varying time intervals. This characteristic inevitably impedes the performance of clinical event prediction (CEP) using the existing approaches. To address this challenge, we propose a novel approach to learn representative and discriminative PTT features for CEP. In detail, multivariate Hawkes process (HP) is adopted to uncover the mutual excitation intensities between clinical event pairs in an interpretable manner. Thereafter, the captured spontaneous and interactional intensities of events are incorporated into recurrent neural networks (RNN) to encode PTT in latent representations, while jointly performing the CEP task based on the extracted trajectory representations. We evaluate the performance of the proposed approach on a real clinical dataset consisting of 13,545 visits of 2,102 heart failure patients. Compared to state-of-the-art methods, our best model achieves 6.4% and 4.1% AUC performance gains on three-months and one-year CEP tasks, respectively. The experimental results demonstrate that the proposed approach outperforms state-of-the-art models in CEP, and can be profitably exploited as a basis for PTT analysis and optimization.