%A Fan Xinyue,Cui Lei %T Using Text Mining to Discover Drug Side Effects: Case Study of PubMed %0 Journal Article %D 2018 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.1047 %P 79-86 %V 2 %N 3 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4487.shtml} %8 2018-03-25 %X

[Objective] This paper finds the potential side effects of drugs with the help of text mining, aiming to improve the contents of existing databases and early prediction of drug side effects. [Methods] A total of 100, 873 articles were retrieved from the PubMed database for about five years (2011-2016). We generated the drug side effects co-occurrence matrix and conducted gCLUTO bi-clustering analysis with Perl’s segmentation technique, named entity recognition method based on the dictionary, as well as the R language. [Results] For one category of results, we found the precision rate of the proposed method reached 75.65%, and identified 13.91% potential side effects. [Limitations] Only used the dictionary-based named entity recognition method and did not consider grammatical or lexis factors, which yielded high false positive rates. [Conclusions] This paper proposes a new approach to detect the unannounced side effects of drugs automatically and effectively.