Exploration and example interpretation of real-world herbal prescription classification based on similarity matching algorithm
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:48 |
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| Enthalten in: |
Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica - 48(2023), 4 vom: 05. Feb., Seite 1132-1136 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Zhao, Guo-Zhen [VerfasserIn] |
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| Links: |
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| Themen: |
English Abstract |
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| Anmerkungen: |
Date Completed 07.03.2023 Date Revised 07.03.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.19540/j.cnki.cjcmm.20221027.501 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM353781649 |
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| 520 | |a 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 | ||
| 650 | 4 | |a English Abstract | |
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| 650 | 4 | |a evidence-based medicine | |
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| 650 | 4 | |a prescription identification | |
| 650 | 4 | |a real-world data | |
| 650 | 4 | |a similarity matching algorithm | |
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| 700 | 1 | |a Yan, Shi-Yan |e verfasserin |4 aut | |
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| 700 | 1 | |a Ye, Hao-Ran |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Li |e verfasserin |4 aut | |
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| 700 | 1 | |a DU, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Fang-Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Qing-Quan |e verfasserin |4 aut | |
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