Yi Mai Zhi Cheng: Injecting Vitality into Traditional Chinese Medicine in the New Era

Abstract

The clinical experience of renowned senior TCM physicians is a valuable asset for the inheritance of traditional Chinese medicine. However, most of this experience exists in the form of tacit knowledge, making it hard to carry out systematic collation through traditional literature. Supported by the college innovation project “Yi Mai Zhi Cheng”, our team explored a human-computer collaborative research route of “in-depth interview + AI quantitative analysis”, aiming to solve the problem of poor replicability of TCM experience. At the preliminary research stage, we formulated a structured interview plan through literature review. Afterwards, we conducted on-site semi-structured interviews with 2 senior TCM physicians for a total of 4 hours. Questionnaires were also used to understand public awareness. During data preprocessing, we transcribed interview recordings of about 10,000 words into text. After cross-checking and terminology standardization, we manually annotated key entities: 320 symptom entries, 85 syndrome entries, 42 prescription entries, 156 Chinese herbal medicine entries and 210 relational pairs. A preliminary experience knowledge graph was constructed. The current phased results have verified the feasibility of this method. In the follow-up study, we will invite young physicians to test the system and further deepen data analysis, so as to provide a replicable paradigm for the digital preservation of TCM practitioners’ experience.