Nguyen Phung Anh, Yang Hsuan-Chia, Xu Rong, Li Yu-Chuan Jack
Department of Population & Quantitative Health Sciences, School of Medicine, Case Western Reserve University, US.
College of Medical Science & Technology, Taipei Medical University, Taiwan.
Stud Health Technol Inform. 2018;247:661-665.
Traditional Chinese Medicine utilization has rapidly increased worldwide. However, there is limited database provides the information of TCM herbs and diseases. The study aims to identify and evaluate the meaningful associations between TCM herbs and breast cancer by using the association rule mining (ARM) techniques. We employed the ARM techniques for 19.9 million TCM prescriptions by using Taiwan National Health Insurance claim database from 1999 to 2013. 364 TCM herbs-breast cancer associations were derived from those prescriptions and were then filtered by their support of 20. Resulting of 296 associations were evaluated by comparing to a gold-standard that was curated information from Chinese-Wikipedia with the following terms, cancer, tumor, malignant. All 14 TCM herbs-breast cancer associations with their confidence of 1% were valid when compared to gold-standard. For other confidences, the statistical results showed consistently with high precisions. We thus succeed to identify the TCM herbs-breast cancer associations with useful techniques.
中医药在全球范围内的使用迅速增加。然而,提供中药草药与疾病信息的数据库有限。本研究旨在通过使用关联规则挖掘(ARM)技术来识别和评估中药草药与乳腺癌之间的有意义关联。我们利用1999年至2013年台湾国民健康保险理赔数据库,对1990万份中药处方应用了ARM技术。从这些处方中得出了364种中药草药与乳腺癌的关联,然后根据其20的支持度进行筛选。通过与一个黄金标准进行比较,对得到的296种关联进行了评估,该黄金标准是从中文维基百科中筛选出的包含“癌症”“肿瘤”“恶性”等关键词的信息。与黄金标准相比,所有14种置信度为1%的中药草药与乳腺癌的关联都是有效的。对于其他置信度,统计结果显示精度一直很高。因此,我们成功地利用有用的技术识别出了中药草药与乳腺癌的关联。