Two level time-aware network for clinical event prediction

Electronic Health Records (EHR) have greatly facilitated predictive models based on deep learning. However, since EHR data contains information features at various levels, existing studies typically focus only on mining information at single level, leading to underutilization of EHR resources and consequently inaccurate predictions of patient conditions. Moreover, existing research often acquires static representations of diseases. In practice, the progression of diseases is dynamic and even the same disease may vary in importance in predicting patient outcomes at different times of visits. To address these issues, we propose a T wolevel, time-aware C linical E vent prediction M odel, TCEM. Specifically, we designed a temporal weight subgraph to model the code level structural features and integrate the dynamic impact of visit timing on disease representation. At the visit level, we categorize diseases based on their occurrence or disappearance in adjacent visits to capture information related to disease evolution. We conducted extensive experiments on two real-world datasets, showing that TCEM outperforms existing models in medical event prediction. Our code can be found at https://github.com/lzh-nwpu/tcem .