Extracting clinical experiences from ancient literature of Traditional Chinese Medicine via deep learning

Abstract:

Ancient literature of Traditional Chinese Medicine (TCM) contains rich clinical experiences, which is the empirical summary of clinical diagnosis and treatment in the process of ancient Chinese medicine practice, and embodies the theoretical framework and ideological basis of the formation and development of TCM. However, due to the volume and dispersion of valuable clinical experiences, it is difficult for TCM doctors to quickly and comprehensively obtain the clinical information they need from ancient literature manually, and the document retrieval tools can only provide documentlevel information screening, which cannot support finegrained information extraction. In addition, the different characteristics of ancient Chinese relative to modern Chinese also limit the use of mainstream text analysis tools. For this reason, we propose a task of information extraction from the ancient literature of TCM for obtaining clinical experiences, which is used to identify text fragments describing clinical experiences in ancient literature and manually annotate sample data for training and testing the extraction task, a sequence labeling model is designed based on deep learning to complete the task. Considering the overfitting problem that can be brought about by the small amount of annotated data, we introduce adversarial training and virtual adversarial training to enhance the generalization ability of the proposed model. A series of sufficient experiments are conducted on the clinical experience dataset to verify the effectiveness of the model, and the experimental results show the feasibility of extracting clinical experiences from ancient literature by information extraction technology, and a promising baseline and a reusable annotated dataset for the new information extraction task are available.