Purpose/Significance To construct a named entity corpus of traditional Chinese medicine(TCM)ancient records,and to improve the recognition accuracy and applicability of the general domain named entity recognition(NER)model in the field of TCM ancient records.Method/Process Annotation standards for entities in TCM ancient records are formulated,and 2 384 Xin'an medical records are annotated.A RoBERTa-BiLSTM-CRF model is developed,and word vectors with semantic features are generated using the RoBERTa pre-trained language model.The BiLSTM-CRF model is used to learn the global semantic features of sequences and decode and output the optimal label sequence.Dictionary and rule features are incorporated to enhance the model's capability to recognize entity boundaries and categories.Result/Conclusion The model shows a good recognition effect on the named entity corpus of Xin'an medical cases.Integration of domain terminology dictionaries and rule-based features improves the overall Fl score to 72.8％.

OBJECTIVE To develop an artificial intelligence-based intelligent auxiliary diagnosis and treatment system for Xin'an medicine to address the challenges of integrating ancient Xin'an medical case records into modern clinical applications.METHODS The project involved structuring and standardizing case records from ancient texts of Xin'an medicine to build a compre-hensive Xin'an medicine database.Advanced techniques,such as data annotation,entity relationship extraction,and data mining,were applied to create a Xin'an medicine knowledge base.Furthermore,a knowledge graph of Xin'an medicine was constructed using techniques for knowledge acquisition,integration,storage,and graph-based question-answering,improving the efficiency of knowl-edge organization and retrieval.The LangChain framework was utilized to connect the Xin'an medicine knowledge base to a large lan-guage model,enabling a model-driven local knowledge base question-answering system.RESULTS The study successfully estab-lished a systematic and standardized knowledge base for Xin'an medical case records.The application of knowledge graph technology provided a clear visualization of Xin'an medicine's knowledge structure,and the development of an intelligent question-answering module significantly improved the efficiency of knowledge management and retrieval.The local knowledge base question-answering sys-tem,powered by a large language model and based on Xin'an medicine's theoretical and practical expertise,delivered accurate diag-nostic and treatment support,promoting the heritage and innovation of Xin'an medicine.CONCLUSION This research validates the feasibility of modernizing traditional medical texts and provides an innovative approach to knowledge development and clinical applica-tion in Chinese medicine.The findings have significant academic value and promising clinical implications.

OBJECTIVE To develop an artificial intelligence-based intelligent auxiliary diagnosis and treatment system for Xin'an medicine to address the challenges of integrating ancient Xin'an medical case records into modern clinical applications.METHODS The project involved structuring and standardizing case records from ancient texts of Xin'an medicine to build a compre-hensive Xin'an medicine database.Advanced techniques,such as data annotation,entity relationship extraction,and data mining,were applied to create a Xin'an medicine knowledge base.Furthermore,a knowledge graph of Xin'an medicine was constructed using techniques for knowledge acquisition,integration,storage,and graph-based question-answering,improving the efficiency of knowl-edge organization and retrieval.The LangChain framework was utilized to connect the Xin'an medicine knowledge base to a large lan-guage model,enabling a model-driven local knowledge base question-answering system.RESULTS The study successfully estab-lished a systematic and standardized knowledge base for Xin'an medical case records.The application of knowledge graph technology provided a clear visualization of Xin'an medicine's knowledge structure,and the development of an intelligent question-answering module significantly improved the efficiency of knowledge management and retrieval.The local knowledge base question-answering sys-tem,powered by a large language model and based on Xin'an medicine's theoretical and practical expertise,delivered accurate diag-nostic and treatment support,promoting the heritage and innovation of Xin'an medicine.CONCLUSION This research validates the feasibility of modernizing traditional medical texts and provides an innovative approach to knowledge development and clinical applica-tion in Chinese medicine.The findings have significant academic value and promising clinical implications.

Objective:To compare the differences in population distribution and prognosis of patients with nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiotherapy (IMRT) in T staging of the Union for International Cancer Control (UICC) 7th edition and UICC 8th edition, and to analyze the prognostic factors in patients with NPC.Methods:The clinicopathologic date of 184 patients with newly diagnosed NPC treated with IMRT at the Department of Radiation Oncology of Weifang People′s Hospital of Shandong Province from June 1, 2005 to December 31, 2017 were retrospectively analyzed. All patients were restaged according to the 7th and 8th edition of the UICC staging system. The distribution of T staging of patients in the two staging systems was analyzed, and the consistency of the two staging systems was compared using the Kappa consistency test. Kaplan-Meier method was used for survival analysis, and log-rank test was used to compare the prognostic differences among T stages. Cox regression model was used to analyze the prognostic factors of patients with NPC.Results:Of all 184 patients with NPC, stage T 1, T 2, T 3 and T 4 respectively accounted for 18.5% (34/184), 16.8% (31/184), 15.2% (28/184) and 49.5% (91/184) according to the 7th edition UICC staging system. However, stage T 1, T 2, T 3 and T 4 respectively accounted for 18.5% (34/184), 34.2% (63/184), 30.4% (56/184) and 16.8% (31/184) according to the 8th edition UICC staging system. The T staging population distribution of the two staging systems showed moderate consistency (Kappa=0.58). There was a statistically significant difference in overall survival (OS) among patients with stage T 1, T 2, T 3, T 4 according to the 7th edition UICC staging system ( χ2=10.606, P=0.014). There were statistically significant differences in OS between stage T 1 and stage T 2, T 3, T 4 ( χ2=4.866, P=0.027; χ2=11.965, P=0.001; χ2=4.351, P=0.037). The OS curves of stage T 2 and T 4 could not be separated. Moreover, the OS curves of stage T 3 and T 4 were distributed in reverse order. There was a statistically significant difference in OS among patients with stage T 1, T 2, T 3, T 4 according to the 8th edition staging system ( χ2=8.663, P=0.034). There were statistically significant differences in OS between stage T 1 and stage T 3, T 4( χ2=8.746, P=0.003; χ2=7.580, P=0.006). The OS curves of stage T 1 to T 4 were distributed in order, but the curves of stage T 3 and T 4 could not be separated. There was a statistically significant difference in progression-free survival (PFS) among patients with stage T 1, T 2, T 3, T 4 according to the 7th edition UICC staging system ( χ2=11.289, P=0.010). There were statistically significant differences in PFS between stage T 1 and stage T 2, T 3, T 4 ( χ2=8.209, P=0.004; χ2=13.302, P<0.001; χ2=6.550, P=0.010). The PFS curves of stage T 2 and T 4 could not be separated. Moreover, the PFS curves of stage T 3 and T 4 were distributed in reverse order. There was a statistically significant difference in PFS among patients with stage T 1, T 2, T 3, T 4 according to the 8th edition staging system ( χ2=12.074, P=0.007). There were statistically significant differences in PFS between stage T 1 and stage T 2, T 3, T 4( χ2=5.182, P=0.023; χ2=11.217, P=0.001; χ2=10.174, P=0.001). The PFS curves of stage T 1 to T 4 were distributed in order, but the curves of stage T 3 and T 4 could not be separated. The results of Cox multivariate analysis showed that T staging of both staging systems were the independent prognostic factors of the OS ( P=0.013; P=0.026) and PFS ( P=0.031; P=0.012). However, T staging of the two editions were not the independent prognostic factors of the local recurrence-free survival (LRFS) ( P=0.351; P=0.167) and distant metastasis-free survival (DMFS) ( P=0.059; P=0.052). The age was the independent prognostic factor of the OS ( HR=2.70, 95% CI: 1.53-4.76, P=0.001; HR=2.74, 95% CI: 1.55-4.84, P=0.001), PFS ( HR=2.72, 95% CI: 1.46-5.08, P=0.002; HR=2.94, 95% CI: 1.57-5.52, P=0.001), LRFS ( HR=5.87, 95% CI: 1.62-21.27, P=0.007; HR=6.02, 95% CI: 1.61-22.49, P=0.008) and DMFS ( HR=2.40, 95% CI: 1.22-4.72, P=0.011; HR=2.63, 95% CI: 1.34-5.18, P=0.005). N staging was the independent prognostic factor of the OS ( P=0.031; P=0.028). Conclusion:The T staging population distribution of the 7th and 8th edition UICC staging system had moderate consistency, and the T staging of the 8th edition is more advantageous in predicting the prognosis of OS and PFS. In both editions, T staging is an independent prognostic factor for OS and PFS.

The development of informatization and modernization of traditional Chinese medicine (TCM) has been restricted to some degree due to the lack of sufficient modern scientific evidence to support TCM theory. Rapid development of computer technology, information and imaging technology, which can be used to explore TCM theory and mechanism, may bring hope to solve this problem. In recent years, functional Magnetic Resonance Imaging (fMRI) has been widely used to study TCM theory and mechanism. However, shortage of interdisciplinary talents those who possess both medical and engineering knowledge has restricted the development of fMRI research in the field of Chinese medicine. With the development of the discipline of TCM information science in TCM colleges and universities, students majoring in TCM information science will be the main source of researchers engaging in TCM fMRI researches. The flourishing development of TCM fMRI researches will cultivate a large number of talents adapting in TCM information science who will promote the construction of TCM information science.