Open access
Artificial intelligence models Traditional Chinese Medicine principles to integrate with biomedicine
Hao-Ran Li
Xing-Ye Cheng
Zi-Yang Huang
Jing-Yuan Luo
Qian-Qian Xu
Qi-Guang Zhao
Tian-Chen Guo
Yu-Meng Zhang
Linda L. D. Zhong
Zhao-Xiang Bian
Lei-Han Tang
Ai-ping Lyu
Liang Tian
Medicine
Abstract
Using 26,339 model‐eligible Traditional Chinese Medicine (TCM) formula records, we trained an autoencoder following TCM's compression–expansion logic to learn the TCM embedding space (TCM‐ES), quantifying symptom‐pattern–formula mappings. TCM‐ES aligns with conventional TCM principles not used in training. Biomedical entities were mapped post hoc into the fixed space, supporting formula prediction, retrospective clinical analyses, and exploratory therapeutic prioritization.