Enhancing diagnosis prediction with adaptive disease representation learning

Diagnosis prediction predicts which diseases a patient is most likely to suffer from in the future based on their historical electronic health records. The time series model can better capture the temporal progression relationship of patient diseases, but ignores the semantic correlation between all diseases; in fact, multiple diseases that are often diagnosed at the same time reflect hidden patterns that are conducive to diagnosis, so predefined global disease co-occurrence graph can help the model understand disease relationships. But it may contain a lot of noise and ignore the semantic adaptation of the disease under the diagnosis target. To this end, we propose a graph-driven end-to-end framework, named Adaptive Disease Representation Learning (ADRL), obtain disease representation after learning complex disease relationships, and then use it to improve diagnosis prediction performance. This model introduces an adaptive mechanism to dynamically adjust and optimize disease relationships by performing self-supervised perturbations on a predefined global disease co-occurrence graph, thereby learning a global disease relationship graph that contains complex semantic association information between diseases. The computational burden of adaptive global disease graph can be further alleviated by the proposed SVD-based accelerator. Finally, experimental results on two real-world EHR datasets show that the proposed model outperforms existing models in diagnosis prediction.