Discovering Time-Aware Hidden Dependencies with Personalized Graphical Structure in Electronic Health Records

Over the past decade, significant advancements in mining electronic health records (EHRs) have enabled a broad range of decision-support applications, and offered an unprecedented capacity for predicting critical events such as disease prognosis and mortality in healthcare. Despite the availability of comprehensive coding systems in EHRs (e.g., ICD-9), which are designed to record diverse information on diseases, procedures, and medications over time, the complex and dynamic dependencies among the recorded data are usually not captured. This limitation often hinders the contextual understanding of medical observations for effective EHR representation learning. Therefore, there is a compelling need to discover a hidden “EHR graph” that represents the medical relationship between the observed features according to a patient’s history. These hidden graphs consisting of the medical codes from the same visits can offer a comprehensive insight derived from disease-to-disease, disease-to-drug, and drug-to-drug dependencies. However, it is still unclear how to address the challenge that the dependencies may vary from patient to patient, and they can dynamically evolve from one visit to another. To this end, we propose Timeaware Personalized Graph Transformer (TPGT), a novel attention-based time-aware hidden graph model, that captures the personalized graphical structures among observed medical codes and summarizes the temporal code dependencies over time to improve patient representation for outcome prediction. Built upon an intra-visit and an inter-visit dual-attention mechanism to model patients’ EHR graphs, our model offers an interpretability of what diagnosis or medication in a patient’s history can interact, and how those interactions may change over time. We conduct extensive experiments on two real-world EHR datasets for different healthcare predictive tasks: acute kidney injury (AKI) prediction and ICU mortality prediction. The experimental results demonstrate a significant performance improvement of the proposed model over baselines through multi-aspect quantitative evaluation. Furthermore, we perform various qualitative studies to validate the interpretability of the model which highlights the application of the proposed method in the context of personalized medicine.