Reinforcement Learning–Based Temporal Knowledge Graph Reasoning for Predicting Chronic Gastritis Diagnosis and Treatment: Development and Validation Study

The useful part

We propose RL4TKGR, a reinforcement learning–based temporal knowledge graph (TKG) reasoning model, to predict a chronic gastritis diagnosis. Methods: RL4TKGR incorporates a reinforcement learning policy network that uses a dual-path encoding module to model historical and nonhistorical diagnostic information separately. This design addresses the strategic bias problem and enables interpretable reasoning.

How it works

• After adding recent TKG reasoning baselines, adaptive path-memory network (DaeMon) achieved the strongest baseline MRR for all 3 subsets, with MRR values of 43.26 (SD 0.95), 51.76 (SD 1.12), and 54.87 (SD...
• ablation experiments and case analyses further supported the contribution of historical modeling components practical utility model predicting disease subtypes therapeutic medications.
• conclusions: this study improves chronic gastritis diagnosis treatment prediction by integrating a history-aware dual-path policy network with dynamic event bala...
• This study aims to dynamically predict chronic gastritis diagnosis.
• Experiments on the self-constructed CG-TKG resulted in RL4TKGR achieving the highest mean reciprocal rank (MRR) on CG-TKG-4 (51.66, SD 0.76), CG-TKG-8 (53.18, SD 0.79), and CG-TKG-12 (56.95, SD 0.84).

What to take from it

Compared with DaeMon, the overall paired tests across MRR and Hits@1/3/10 yielded????<.001 for all subsets.

Details worth keeping

The clinical progression of chronic gastritis involves intricate temporal dependencies, which makes it difficult to capture both the dynamic trajectory of the disease and the underlying relationships among medical events using conventional methods.