Multi-strategy RAG for Disease Comorbidity Prediction.

Abstract

Large Language Models (LLMs) are increasingly demonstrating considerable promise in predicting disease comorbidities. Although structural data from disease networks and retrieval-augmented generation (RAG) have been adopted to enhance disease comorbidity prediction, three underlying limitations remain. (i) Current RAGs implementations often compromise semantic coherence by relying on fixed chunking strategies. (ii) Structural information for disease networks is generally extracted using a single metric, regardless of potential disease associations driven by complex biological mechanisms. (iii) Conventional Chain-of-Thought (CoT) prompts are normally susceptible to error propagation due to their dependence on coarse-grained entity extraction. To overcome these limitations, this study introduced a novel multi-strategy RAG framework integrating a dual-track retrieval approach, a multi-stage CoT strategy, and a multi-metric heuristic to support high-quality, domain-specific reasoning. Built upon a dynamic vector database, the dual-track retrieval mechanism identified implicit relationships among diseases. We also developed a specialized dynamic ensemble heuristic strategy, which adapts supervised ensemble methods to improve disease association prediction by integrating multiple topological metrics. Furthermore, a multi-stage CoT strategy was further developed to enhance the transparency and robustness of the inference process. Finally, a dynamic selection mechanism was applied to adaptively integrate structured signals with semantic signals for optimized predictive performance. Extensive evaluations across multiple benchmarks indicated that our framework exceeded state-of-the-art baselines, registering AUC increases of 10.41% and 5.88%, Average Precision (AP) increases of 9.70% and 5.10%, and F1-score increases of 8.37% and 3.65%, respectively. Further experiments under zero-shot, few-shot, and ablation settings validated the model's effectiveness, generalizability, and interpretability.