A Machine Learning Approach to Classification of Case Reports on Adverse Drug Reactions Using Text Mining of Expert Opinions
In this paper, we present a machine-learning approach to classify case reports on adverse drug reactions according to the causal relationship of adverse drug reactions (ADR). For this purpose, the Naïve Bayes classification algorithm is combined with text mining technique, and applied to textual data of expert opinion on ADR case reports in the Korea Adverse Event Reporting System database. The proposed algorithm classifies the case reports into three categories such as possible, probable, and unlikely based on the causal relationship. Our experimental results show that the precision and recall of data belonging to possible is much higher than the other data belonging to probable and unlikely. We believe that our approach can be used not only for signal but also for prediction and prevention of ADRs.
KeywordsNaïve Bayes classifier Classification of case reports Causal relationship of ADRs Combining text mining and machine learning
This research was supported by a grant (16172MFDS163) from a Ministry of Food and Drug Safety in 2016.
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