Interpretable Disease Progression Prediction Based on Reinforcement Reasoning Over a Knowledge Graph

Objective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease progression prediction task as a random walk along a knowledge graph (KG). Specifically, we build a KG to record relationships between diseases and risk factors according to validated medical knowledge. Then, an object walks along the KG. It starts walking at a patient entity, which connects the KG based on the patient's current diseases or risk factors and stops at a disease entity representing the predicted disease. The trajectory generated by the object represents an interpretable disease progression path of the given patient. The dynamics of the object are controlled by a policy-based reinforcement learning module, which is trained by electronic health records (EHRs). Experiments: We utilized three real-world EHR datasets to evaluate the performance of our model. In the disease progression prediction task, our model achieves 0.743, 0.639, and 0.643 in terms of macro area under the curve (AUC) in predicting 53 circulation system diseases in the three datasets, respectively. This performance is comparable to medical research's commonly used machine learning models. In qualitative analysis, our clinical collaborator reviewed the disease progression paths generated by our model and advocated their interpretability and reliability. Conclusion: Experimental results validate the proposed model in interpretably evaluating and optimizing disease progression prediction. Significance: Our work contributes to leveraging the potential of medical knowledge and medical data jointly for interpretable prediction tasks.