Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs

TL;DR

This paper introduces a reinforcement learning-based method that traces disease progression paths on a medical knowledge graph, enabling interpretable and accurate disease risk prediction from electronic health records.

Contribution

It formulates disease prediction as a path-finding problem on a knowledge graph controlled by reinforcement learning, integrating medical knowledge with patient data for interpretability.

Findings

Achieves macro AUC of 0.743 and 0.639 on two datasets, comparable to traditional ML models.

Provides interpretable disease progression paths validated by clinical experts.

Demonstrates the effectiveness of combining medical knowledge with data-driven methods.

Abstract

Objective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease 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, a mathematical object walks along the KG. It starts walking at a patient entity, which connects the KG based on the patient current diseases or risk factors and stops at a disease entity, which represents 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 (RL) module, which is trained by electronic health records (EHRs). Experiments: We utilized two real-world EHR datasets to evaluate the performance of our model. In the disease prediction task, our model achieves 0.743 and 0.639 in terms of macro area under the curve (AUC) in predicting 53 circulation system diseases in the two datasets, respectively. This performance is comparable to the commonly used machine learning (ML) models in medical research. 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 prediction. Significance: Our work contributes to leveraging the potential of medical knowledge and medical data jointly for interpretable prediction tasks.