Exploration and example interpretation of real-world herbal prescription classification based on similarity matching algorithm.
10.19540/j.cnki.cjcmm.20221027.501
- Author:
Guo-Zhen ZHAO
1
;
Hai-Tian LU
1
;
Shi-Yan YAN
2
;
Yu-Hong GUO
3
;
Hao-Ran YE
3
;
Li JIANG
4
;
Yao-Fu ZHANG
4
;
Jing HU
3
;
Shi-Qi GUO
2
;
Yuan DU
3
;
Fang-Yu LIU
3
;
Bo LI
3
;
Qing-Quan LIU
3
Author Information
1. Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Chinese Medicine,Beijing Evidence-based Chinese Medicine Center Beijing 100010, China Beijing University of Chinese Medicine Beijing 100029, China.
2. Beijing University of Chinese Medicine Beijing 100029, China.
3. Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Chinese Medicine,Beijing Evidence-based Chinese Medicine Center Beijing 100010, China.
4. Beijing University of Chinese Medicine Beijing 100029, China Dongzhimen Hospital, Beijing University of Chinese Medicine Beijing 100700, China.
- Publication Type:Journal Article
- Keywords:
evidence-based medicine;
prescription classification;
prescription identification;
real-world data;
similarity matching algorithm
- MeSH:
Humans;
COVID-19;
Algorithms;
Databases, Factual;
Prescriptions;
Plant Extracts
- From:
China Journal of Chinese Materia Medica
2023;48(4):1132-1136
- CountryChina
- Language:Chinese
-
Abstract:
In observational studies, herbal prescriptions are usually studied in the form of "similar prescriptions". At present, the classification of prescriptions is mainly based on clinical experience judgment, but there are some problems in manual judgment, such as lack of unified criteria, labor consumption, and difficulty in verification. In the construction of a database of integrated traditional Chinese and western medicine for the treatment of coronavirus disease 2019(COVID-19), our research group tried to classify real-world herbal prescriptions using a similarity matching algorithm. The main steps include 78 target prescriptions are determined in advance; four levels of importance labeling shall be carried out for the drugs of each target prescription; the combination, format conversion, and standardization of drug names of the prescriptions to be identified in the herbal medicine database; calculate the similarity between the prescriptions to be identified and each target prescription one by one; prescription discrimination is performed based on the preset criteria; remove the name of the prescriptions with "large prescriptions cover the small". Through the similarity matching algorithm, 87.49% of the real prescriptions in the herbal medicine database of this study can be identified, which preliminarily proves that this method can complete the classification of herbal prescriptions. However, this method does not consider the influence of herbal dosage on the results, and there is no recognized standard for the weight of drug importance and criteria, so there are some limitations, which need to be further explored and improved in future research.
