Knowledge-Enhanced Difference-Aware Clinical Time Series Representation Learning for Diagnosis Prediction.

Predicting future health status based on historical patient visits is one of the essential tasks in healthcare. Many existing approaches attempt to enhance the representation learning capability of models by incorporating relevant medical knowledge, but their effectiveness is severely affected by the incompleteness and noise of the knowledge graphs. Moreover, due to the inability to capture temporal features at a fine-grained level, most existing methods also have limitations in learning the temporal development of patients’ health status. To address these issues, we propose a Knowledge-Enhanced Difference-Aware clinical time series representation learning model (KEDA) for diagnosis prediction. In this model, we first combine the medical ontology graph and co-occurrence graph, and use hierarchical graph convolution and contrastive learning methods to enhance the semantic representation of medical entities. After that, a task-specific difference-aware temporal module is designed to improve the accuracy of patient representation, which adds two novel gated units in the original GRU to fuse multi-type clinical information based on the relationship between different types of data and prediction tasks and capture fine-grained temporal evolution of patient health status. We validate our model on two publicly available datasets, and the experimental results demonstrate that KEDA outperforms the state-of-the-art methods.