ArticleEvidence-based complementary and alternative medicine : eCAM2016
The Common Prescription Patterns Based on the Hierarchical Clustering of Herb-Pairs Efficacies.
Article in Evidence-based complementary and alternative medicine : eCAM, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Review
- Analysis of medication rule of Traditional Chinese Medicine in treating depression based on data mining.Heliyon · 2024Article
- Data mining-based analysis on medication rules of Chinese herbal medicine treating headache with blood stasis syndrome.Heliyon · 2023Article
- On the Core Prescriptions and Their Mechanisms of Traditional Chinese Medicine in Hepatitis B, Liver Cirrhosis, and Liver Cancer Treatment.Journal of oncology · 2022Article
- Data Mining-Based Analysis of Chinese Medicinal Herb Formulae in Chronic Kidney Disease Treatment.Evidence-based complementary and alternative medicine : eCAM · 2020Article
- Multistage analysis method for detection of effective herb prescription from clinical data.Frontiers of medicine · 2018Observational
- Identification of dysregulated genes in rheumatoid arthritis based on bioinformatics analysis.PeerJ · 2017Article
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Authors and funding
1 author.
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Abstract
Prescription patterns are rules or regularities used to generate, recognize, or judge a prescription. Most of existing studies focused on the specific prescription patterns for diverse diseases or syndromes, while little attention was paid to the common patterns, which reflect the global view of the regularities of prescriptions. In this paper, we designed a method CPPM to find the common prescription patterns. The CPPM is based on the hierarchical clustering of herb-pair efficacies (HPEs). Firstly, HPEs were hierarchically clustered; secondly, the individual herbs are labeled by the HPEC (the clusters of HPEs); and then the prescription patterns were extracted from the combinations of HPEC; finally the common patterns are recognized statistically. The results showed that HPEs have hierarchical clustering structure. When the clustering level is 2 and the HPEs were classified into two clusters, the common prescription patterns are obvious. Among 332 candidate prescriptions, 319 prescriptions follow the common patterns. The description of the patterns is that if a prescription contains the herbs of the cluster (C 1), it is very likely to have other herbs of another cluster (C 2); while a prescription has the herbs of C 2, it may have no herbs of C 1. Finally, we discussed that the common patterns are mathematically coincident with the Blood-Qi theory.
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