Research on the medication regularity of traditional Chinese medicine for common chronic diseases based on association rules

Renmin Wang, Jie Li, Yuanyuan Wang
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Abstract

Chronic diseases are the kind of diseases that cause the most severe disease burden in China and have brought significant challenges to the health of our people. With the increase of its global prevalence, it has become a serious global public health problem. Association rules can be used to mine the high-frequency groups of traditional Chinese medicine treating common chronic diseases and the strong association between them and find valuable information hidden in medical data sets. This study uses the FP-growth algorithm to mine and analyzes the Chinese patent medicine prescriptions for five common chronic diseases. The primary purpose is to use association rule mining technology to mine the hidden patterns in traditional Chinese medicine prescriptions for treating chronic diseases and to provide chronic disease medical personnel and related researchers with the characteristics and laws of traditional Chinese medicine for treating chronic diseases, which has significant theoretical value for further understanding and innovating traditional Chinese medicine treatment methods for chronic diseases.
基于关联规则的常见慢性病中药用药规律研究
慢性病是中国疾病负担最重的疾病,对人民健康构成重大挑战。随着其全球患病率的上升,它已成为一个严重的全球公共卫生问题。关联规则可以挖掘中医治疗常见慢性病的高频群和它们之间的强关联,发现隐藏在医疗数据集中的有价值的信息。本研究采用FP-growth算法对五种常见慢性疾病的中成药方剂进行挖掘和分析。主要目的是利用关联规则挖掘技术挖掘治疗慢性病的中药处方中的隐藏模式,为慢性病医务人员和相关研究人员提供中医治疗慢性病的特点和规律,对进一步认识和创新中医治疗慢性病的方法具有重要的理论价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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