[Objective] : To improve the accuracy and professionalism of question-answering (QA) model in traditional Chinese medicine (TCM) lung cancer by integrating large language models with structured knowledge graphs using the knowledge graph (KG) to text-enhanced retrieval-augmented generation (KG2TRAG) method. [Methods] : The TCM lung cancer model (TCMLCM) was constructed by fine-tuning ChatGLM2-6B on the specialized datasets Tianchi TCM, HuangDi, and ShenNong-TCM-Dataset, as well as a TCM lung cancer KG. The KG2TRAG method was applied to enhance the knowledge retrieval, which can convert KG triples into natural language text via ChatGPT-aided linearization, leveraging large language models (LLMs) for context-aware reasoning. For a comprehensive comparison, MedicalGPT, HuatuoGPT, and BenTsao were selected as the baseline models. Performance was evaluated using bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting evaluation (ROUGE), accuracy, and the domain-specific TCM-LCEval metrics, with validation from TCM oncology experts assessing answer accuracy, professionalism, and usability. [Results] : The TCMLCM model achieved the optimal performance across all metrics, including a BLEU score of 32.15%, ROUGE-L of 59.08%, and an accuracy rate of 79.68%. Notably, in the TCM-LCEval assessment specific to the field of TCM, its performance was 3% − 12% higher than that of the baseline model. Expert evaluations highlighted superior performance in accuracy and professionalism. [Conclusion] TCMLCM can provide an innovative solution for TCM lung cancer QA, demonstrating the feasibility of integrating structured KGs with LLMs. This work advances intelligent TCM healthcare tools and lays a foundation for future AI-driven applications in traditional medicine.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.