Current status, challenges and countermeasures of acupuncture data mining literature researches

Abstract

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.