The class imbalance problem detecting adverse drug reactions in electronic health records.

This work focuses on adverse drug reaction extraction tackling the class imbalance problem. Adverse drug reactions are infrequent events in electronic health records, nevertheless, it is compulsory to get them documented. Text mining techniques can help to retrieve this kind of valuable information from text. The class imbalance was tackled using different sampling methods, cost-sensitive learning, ensemble learning and one-class classification and the Random Forest classifier was used. The adverse drug reaction extraction model was inferred from a dataset that comprises real electronic health records with an imbalance ratio of 1:222, this means that for each drug-disease pair that is an adverse drug reaction, there are approximately 222 that are not adverse drug reactions. The application of a sampling technique before using cost-sensitive learning offered the best result. On the test set, the f-measure was 0.121 for the minority class and 0.996 for the majority class.