To address challenges such as diverse clinical data sources, inconsistent standards, and difficulties in sharing, this study developed a clinical data standardization system based on medical data element and Large Language Model (LLM). 1. Customizable patient and medical record templates were created using medical data element and related terminology, enabling dynamic binding for data structuring and standardization. 2. LLM was leveraged to perform intelligent information extraction, semantic normalization, and standardization of unstructured or semi-structured clinical text. 3. User permissions were configured to manage medical record data across both record repositories and institutions, supporting cross-repository and cross-institutional export of standardized data. This study offers an alternative solution for integrating multi-source, heterogeneous clinical data while providing high-quality AI-ready data for applications such as data mining and LLM training. This advancement supports research in clinical science, medical artificial intelligence, and related fields.

Ontology is essential for representing semantics and knowledge. This study compares three Large Language Models (LLM)-assisted ontology construction methods- no prompting, chain - of - thought prompting accompanied by a guiding table, and the combination of ISO standards and chain - of - thought prompting accompanied by a guiding table. And applies them to tongue diagnosis ontology in Traditional Chinese Medicine. The construction process systematically leverages LLMs' generative and analytical capabilities, following a structured workflow of domain definition, terminology collection, class hierarchy construction, and manual verification, with continuous expert involvement to refine definitions and validate outputs. Evaluation based on ISO standards entity alignment and semantic similarity shows that the combination of ISO standards and chain - of - thought prompting accompanied by a guiding table method performs best. The resulting ontology also demonstrate logical consistency, structural soundness, semantic accuracy, and inferential completeness.

As an emerging interdisciplinary discipline, Traditional Chinese Medicine Informatics (TCMI) faces the challenges of improving its training mode, adjusting its curriculum, and optimizing teaching resources during its development. In this study, the academic master's degree students of TCMI jointly cultivated by the School of Pharmaceutical Information of Changchun University of Traditional Chinese Medicine and the Institute of Traditional Chinese Medicine Information of China Academy of Traditional Chinese Medicine are selected as the research object to explore the new mode of joint training of academic postgraduates between the universities and the institute. With the help of an Internet platform, a "self-organized" learning platform is constructed to guide students to realize self-management and self-improvement, and a new "student-led" cultivation mode is gradually built on this basis. We hope to provide innovative methods for developing discipline, valuable ideas, and inspiration for improving the cultivation mode

Objective: In this study, we aim to investigate the utilization of large language models (LLMs) for traditional Chinese medicine (TCM) formula classification by fine-tuning the LLMs and prompt template. Methods: We refined and cleaned the data from the Coding Rules for Chinese Medicinal Formulas and Their Codes [1], the Chinese National Medical Insurance Catalog for Proprietary Chinese Medicines [2], and Textbooks of Formulas of Chinese Medicine [3] to address the standardization of TCM formula information, and finally we extracted 2308 TCM formula data as a dataset in this study. We designed a prompt template for the TCM formula classification task and randomly divided the formula dataset into three subsets: a training set (2000 formulas), a test set (208 formulas), and a validation set (100 formulas). We fine-tuned the open-source LLMs such as ChatGLM-6b and ChatGLM2-6b. Finally, we evaluate all selected LLMs in our study: ChatGLM-6b (original), ChatGLM2-6b (original), ChatGLM-130b, InternLM-20b, ChatGPT, ChatGLM-6b (fine-tuned), and ChatGLM2-6b (fine-tuned). Results: The results showed that ChatGLM2-6b (fine-tuned) and ChatGLM-6b (fine-tuned) achieved the highest accuracy rates of 71% and 70% on the validation set, respectively. The accuracy rates of other models were ChatGLM-130b 58%, ChatGPT 53%, InternLM-20b 52%, ChatGLM2-6b (original) 41%, and ChatGLM-6b (original) 23%. Conclusion: LLMs achieved an impressive 71% accuracy in the formula classification task in our study. This was achieved through fine-tuning and the utilization of prompt templates. And provided a novel option for the utilization of LLMs in the field of TCM.

Objective: To update and upgrade the semantic annotation system [1] for Traditional Chinese Medicine(TCM) literatures developed by our team in the previous period, oriented to the actual application requirements. Methods: The workflow and functional modules of the semantic annotation system are updated and upgraded to meet the functional requirements of practical application scenarios, and special functions are developed. Results: Based on the previous system, the BaiBu Knowledge Engine was upgraded and developed with new functions such as setting and visualizing the multi-level structure of entities and semantic relations at the schema layer and event annotation, and we have improved the model, and adding new functions such as semantic search of the system's front-end knowledge base and visualization of the knowledge sources for knowledge traceability, so as to improve the annotation personnel's knowledge and knowledge management skills, the new features include semantic search and visualization of knowledge sources for knowledge traceability in the front-end of the system, in order to improve the annotation efficiency of the annotators and to express the deep implicit knowledge of TCM’s literature. Conclusion: The updated and upgraded BaiBu Knowledge Engine has been verified and put into use in actual projects, which can provide powerful support for the expression and deep utilization of the knowledge of TCM literature, and realize data integration and knowledge fusion at the semantic level of TCM.

Objective: To construct a natural language processing (NLP) system focused on named entity recognition (NER) and semantic relation extraction (RE) of ancient Chinese medical books, it supports annotated corpora management and semantic knowledge retrieval. Methods: We integrate the 47 ontologies and terminologies as the terminology database. After that, we trained a preprocessing NER model using spaCy and used a hybrid approach combining automated annotation and manual review to annotate corpora of ancient Chinese medical books. Results: The semantic annotation system of Chinese ancient texts named traditional Chinese medicine - semantic annotation system (TCM-SAS), was constructed based on ontologies and terminologies. Annotations and knowledge retrieval of TCM's ancient texts were realized. Conclusion: TCM-SAS is a user-friendly semantic annotation system for ancient Chinese medical books that includes a large-scale manual annotation of TCM literature and semantic knowledge of TCM. TCM-SAS could provide users with two modes of automatic and manual NER and RE for ancient Chinese texts, as well as annotated entity and corpora management. Support the discovery of new knowledge from ancient Chinese medical texts in the future.

Objective: To construct the ontology of interaction between Chinese medicines and chemical medicine, and use ontology reasoning tools to automatically discover the results of interaction between Chinese medicines and chemical medicine. Methods: Based on the analysis of drug safety rules and the principle of drug interaction, the user-defined rules of ontology reasoning tool are designed with ontology language. Results: The ontology of interaction between Chinese medicines and chemical medicine was constructed, and the reasoning from drug interaction to use risk was realized by using ontology reasoning tools. Conclusion: The interaction mode between Chinese medicines and chemical medicine is complex. The construction and research of ontology reasoning of the interaction ontology of Chinese medicines and chemical medicine can provide ideas and methods for the research, but further research is needed.

Psoriasis is a chronic, recurrent, inflammatory skin disease, with varied incidence rates for various populations in different regions of the world. According to statistics, the prevalence of psoriasis in European countries is about 1% ∼ 3%, and 0.47% for China in 2008, which appears on the rise year by year. [1,2] Evidence suggests that genetic and environmental factors play important roles in the pathogenesis of psoriasis [1,2], but its exact etiology and pathogenesis are still unclear. Traditional Chinese medicine (TCM) treatment, based on syndrome differentiation, has a long history in treating this disease and achieved a significant effect in practice [3,4]. However, few studies on intelligent knowledge-based system (KBS), based on TCM treatment, for automatic reasoning and semantic retrieval have been reported, but much less for mature products supporting clinical practice. In order to inherit and develop the excellent tradition of TCM, with psoriasis as a use case, a knowledge base of psoriasis disease was tried to be constructed based on TCM Ontology combined with ontology, knowledge base and disease knowledge related to psoriasis in this paper, which can provide references for the construction of a shared and reusable diagnosis knowledge-base for the psoriasis diagnosis system of TCM experts.