Korean Journal of Clinical Pharmacy (한국임상약학회지)

Korean College Of Clinical Pharmacy (한국임상약학회)
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Analysis of Adverse Drug Reaction Reports using Text Mining

텍스트마이닝을 이용한 약물유해반응 보고자료 분석

  • Kim, Hyon Hee (College of Information Science, Dongduk Women's University) ;
  • Rhew, Kiyon (College of Pharmacy, Dongduk Women's University)
  • 김현희 (동덕여자대학교 정보과학대학) ;
  • 유기연 (동덕여자대학교 약학대학)
  • Received : 2017.09.15
  • Accepted : 2017.12.06
  • Published : 2017.12.29
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Abstract

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

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References

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