Venous Thrombosis Risk Assessment Based on Retrieval-Augmented Large Language Models and Self-Validation

Venous thromboembolism is a disease with high incidence and fatality rate, and the coverage rate of prevention and treatment is insufficient in China. Aiming at the problems of low efficiency, strong subjectivity, and low extraction and utilization of electronic medical record data by traditional evaluation methods, this study proposes a multi-scale adaptive evaluation framework based on retrieval-augmented generation. In this framework, we first optimize the knowledge base construction through entity–context dynamic association and Milvus vector retrieval. Next, the Qwen2.5-7B large language model is fine-tuned with clinical knowledge via Low-Rank Adaptation technology. Finally, a generation–verification closed-loop mechanism is designed to suppress model hallucination. Experiments show that the accuracy of the framework on the Caprini, Padua, Wells, and Geneva scales is 79.56%, 88.32%, 90.51%, and 84.67%, respectively. The comprehensive performance is better than that of clinical expert evaluation, especially in complex cases. The ablation experiments confirmed that the entity–context association and self-verification augmentation mechanism contributed significantly to the improvement in evaluation accuracy. This study not only provides a high-precision, traceable intelligent tool for VTE clinical decision-making, but also validates the technical feasibility, and will further explore multi-modal data fusion and incremental learning to optimize dynamic risk assessment in the future.