Intelligent Prescription-Generating Models of Traditional Chinese Medicine Based on Deep Learning

作者：Qingyang Shi, Lizi Tan, Lim Lian Seng, Huijun Wang · 发表于：World Journal of Traditional Chinese Medicine · 年份：2021 · DOI：10.4103/wjtcm.wjtcm_54_21 · 被引用次数：15 · 研究领域：Traditional Chinese Medicine Studies、Ideological and Political Education、Biomedical Text Mining and Ontologies

Objective: This study aimed to construct an intelligent prescription-generating (IPG) model based on deep-learning natural language processing (NLP) technology for multiple prescriptions in Chinese medicine. Materials and Methods: We selected the Treatise on Febrile Diseases and the Synopsis of Golden Chamber as basic datasets with EDA data augmentation, and the Yellow Emperor's Canon of Internal Medicine, the Classic of the Miraculous Pivot, and the Classic on Medical Problems as supplementary datasets for fine-tuning. We selected the word-embedding model based on the Imperial Collection of Four, the bidirectional encoder representations from transformers (BERT) model based on the Chinese Wikipedia, and the robustly optimized BERT approach (RoBERTa) model based on the Chinese Wikipedia and a general database. In addition, the BERT model was fine-tuned using the supplementary datasets to generate a Traditional Chinese Medicine-BERT model. Multiple IPG models were constructed based on the pretraining strategy and experiments were performed. Metrics of precision, recall, and F1-score were used to assess the model performance. Based on the trained models, we extracted and visualized the semantic features of some typical texts from treatise on febrile diseases and investigated the patterns. Results: Among all the trained models, the RoBERTa-large model performed the best, with a test set precision of 92.22%, recall of 86.71%, and F1-score of 89.38% and 10-fold cross-validation pr...