텍스트마이닝을 이용한 약물유해반응 보고자료 분석
Analysis of Adverse Drug Reaction Reports using Text Mining
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참고문헌
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초록
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, extractingmeaningful 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 textanalysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text ofKAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the wholetext, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis areperformed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Amongtotal 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 inADRs research is able to play an important role in detecting signal information and prediction of ADRs.
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, extractingmeaningful 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 textanalysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text ofKAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the wholetext, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis areperformed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Amongtotal 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 inADRs research is able to play an important role in detecting signal information and prediction of ADRs.
참고문헌 (19)
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