Analysis and prospects of common problems in clinical data mining of traditional Chinese medicine prescriptions.
10.19540/j.cnki.cjcmm.20230512.501
- Author:
Wen-Chao DAN
1
;
Guo-Zhen ZHAO
2
;
Qing-Yong HE
3
;
Hui ZHANG
3
;
Bo LI
2
;
Guang-Zhong ZHANG
1
Author Information
1. Department of Dermatology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University Beijing 100010, China.
2. Beijing Hospital of Traditional Chinese Medicine, Capital Medical University/Beijing Institute of Chinese Medicine/Beijing Center for Evidence-based Traditional Chinese Medicine Beijing 100010, China.
3. Department of Cardiovascular Medicine, Guang'anmen Hospital, China Academy of Chinese Medical Sciences Beijing 100053, China.
- Publication Type:Journal Article
- Keywords:
data mining;
statistical specifications;
traditional Chinese medicine
- MeSH:
Humans;
Medicine, Chinese Traditional;
Prescriptions;
Data Mining;
Cluster Analysis;
Physicians;
Drugs, Chinese Herbal/therapeutic use*
- From:
China Journal of Chinese Materia Medica
2023;48(17):4812-4818
- CountryChina
- Language:Chinese
-
Abstract:
Mining data from traditional Chinese medicine(TCM) prescriptions is one of the important methods for inheriting the experience of famous doctors and developing new drugs. However, current research work has problems such as to be optimized research plans and non-standard statistics. The main problems and corresponding solutions summarized by the research mainly include four aspects.(1)The research plan design needs to consider the efficacy and quality of individual cases.(2)The significance of the difference in confidence order of association rules needs to be further considered, and the lift should not be ignored.(3)The clustering analysis steps are complex. The selection of clustering variables should comprehensively consider factors such as the frequency of TCM, network topology parameters, and practical application significance. The selection of distance calculation and clustering methods should be improved based on the characteristics of TCM clinical data. Jaccard distance and its improvement plan should be given attention in the future. A single, unexplained clustering result should not be presented, but the final clustering plan should be selected based on a comprehensive consideration of TCM clinical characteristics and objective evaluation indicators for clustering.(4)When calculating correlation coefficients, algorithms that are only suitable for continuous variables should not be applied to binary variables. This article explained the connotations of the above problems based on the characteristics of TCM clinical research and statistical principles and proposed corresponding suggestions to provide important references for future data mining research work.
