1
|
Dong X, Zhao C, Song X, Zhang L, Liu Y, Wu J, Xu Y, Xu N, Liu J, Yu H, Yang K, Zhou X. PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning. J Am Med Inform Assoc 2024:ocae066. [PMID: 38598532 DOI: 10.1093/jamia/ocae066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/21/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024] Open
Abstract
OBJECTIVES Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios. MATERIALS AND METHODS We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making. RESULTS To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline). DISCUSSION The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings. CONCLUSION Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.
Collapse
Affiliation(s)
- Xin Dong
- Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Chenxi Zhao
- Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Xinpeng Song
- Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Lei Zhang
- National Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Yu Liu
- Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Jun Wu
- Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Yiran Xu
- Department of Computer Science, Cornell University, New York, NY 14853, United States
| | - Ning Xu
- National Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Jialing Liu
- Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Haibin Yu
- The First Affiliated Hospital, Henan University of Chinese Medicine, Zhengzhou 450000, China
- Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-Constructed by Henan Province & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou 450046, China
| | - Kuo Yang
- Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Xuezhong Zhou
- Beijing Key Lab of Traffic Data Analysis and Mining, Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| |
Collapse
|