Knowledge Graph Enhanced Transformers for Diagnosis Generation of Chinese Medicine.
10.1007/s11655-023-3612-5
- Author:
Xin-Yu WANG
1
;
Tao YANG
1
;
Xiao-Yuan GAO
1
;
Kong-Fa HU
2
Author Information
1. School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
2. School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, China. kfhu@njucm.edu.cn.
- Publication Type:Journal Article
- Keywords:
Chinese medicine diagnosis;
knowledge graph enhanced transformer;
text generation
- MeSH:
Humans;
Medicine, Chinese Traditional;
Pattern Recognition, Automated;
Asian People;
Language;
Learning
- From:
Chinese journal of integrative medicine
2024;30(3):267-276
- CountryChina
- Language:English
-
Abstract:
Chinese medicine (CM) diagnosis intellectualization is one of the hotspots in the research of CM modernization. The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues, however, it is difficult to solve the problems such as excessive or similar categories. With the development of natural language processing techniques, text generation technique has become increasingly mature. In this study, we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues. The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory (BILSTM) with Transformer as the backbone network. Meanwhile, the CM diagnosis generation model Knowledge Graph Enhanced Transformer (KGET) was established by introducing the knowledge in medical field to enhance the inferential capability. The KGET model was established based on 566 CM case texts, and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence (LSTM-seq2seq), Bidirectional and Auto-Regression Transformer (BART), and Chinese Pre-trained Unbalanced Transformer (CPT), so as to analyze the model manifestations. Finally, the ablation experiments were performed to explore the influence of the optimized part on the KGET model. The results of Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2 and Edit distance of KGET model were 45.85, 73.93, 54.59 and 7.12, respectively in this study. Compared with LSTM-seq2seq, BART and CPT models, the KGET model was higher in BLEU, ROUGE1 and ROUGE2 by 6.00-17.09, 1.65-9.39 and 0.51-17.62, respectively, and lower in Edit distance by 0.47-3.21. The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance. Additionally, the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results. In conclusion, text generation technology can be effectively applied to CM diagnostic modeling. It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models. CM diagnostic text generation technology has broad application prospects in the future.
