Evidence map›Paper›PMID 27190534›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2016

The Common Prescription Patterns Based on the Hierarchical Clustering of Herb-Pairs Efficacies.

Jia Cao

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Data Mining-Based Analysis of Chinese Medicinal Herb Formulae in Chronic Kidney Disease Treatment.Evidence-based complementary and alternative medicine : eCAM · 2020
    Article
  6. Observational
  7. Article
4 · The record

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5 · Who and what money

Authors and funding

1 author.

Jia CaoDepartment of Information Technology, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0002-8579-8159

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID27190534
PMCPMC4842035

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