Data mining is an analytical method for revealing the implicit internal relations among the data elements

and is also widely used in the field of acupuncture and moxibustion of traditional Chinese medicine. However

there exist some significant deficits in the rationality of design and implementation

preciseness

repeatability

comprehensiveness

objectivity and depth of insights in some current acupuncture data mining researches. In the present paper

we summarized the literature on acupuncture data mining published in the past five years

and analyzed their common shortcomings in the design

implementation and reporting process

including too broad research scope

fuzzy and limited descriptions about the inclusion criteria

not definite retrieval scope and strategy

rarely seen original researches about the assessment of the report quality

lack of detailed descriptions about the literature screening and data processing procedure

incomplete narration about the research outcomes and their clinical significance

and lack of comprehensiveness and subjectiveness and available coping strategies in the discussion of the research papers

etc.

in order to promote the improvement of literature methodology and quality of acupuncture data mining research

and then improve the reliability and clinical reference value of such research results.