JMIR Publications

ABSTRACT

Background:

The clinical progression of chronic gastritis entails intricate temporal dependencies, making it difficult for conventional methods to capture both the disease’s dynamic trajectory and the underlying relationships among medical events.

Objective:

To predict chronic gastritis diagnosis dynamically, we proposed RL4TKGR, a Reinforcement Learning-based Temporal Knowledge Graph Reasoning model for chronic gastritis diagnosis prediction.

Methods:

Specifically, RL4TKGR incorporated an innovative reinforcement learning policy network that employed a dual-path encoding module to separately model historical and non-historical diagnostic information. Furthermore, RL4TKGR integrated a dual-channel reward function with a dynamic weight allocation mechanism, which adaptively balanced the two information sources, thereby addressing the strategic bias problem and enabling interpretable reasoning.

Results:

Experiments conducted on the self-constructed Chronic Gastritis Temporal Knowledge Graphs (CG-TKG) demonstrated that RL4TKGR delivered state-of-the-art performance across MRR and Hits@1/3/10 metrics, outperforming existing baseline models. Ablation experiments and case analyses further validated the efficacy of each module design and the model's practical utility in predicting disease subtypes and therapeutic medications.

Conclusions:

This paper advanced the predictive accuracy of chronic gastritis while establishing a novel and effective paradigm for interpretable reasoning over complex temporal medical data.