Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use R o BERT a (2020)

Abstract: This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR) tweets from nonADR tweets and is treated as binary classification. Task 3 involves extracting ADR mentions and then mapping them to MedDRA codes. Extracting ADR mentions is treated as sequence labeling and normalizing ADR mentions is treated as multi-class classification. Our system is based on pre-trained language model RoBERTa and it achieves a) F1-score of 58% in task 2 which is 12% more than the average score b) relaxed F1-score of 70.1% in ADR extraction of task 3 which is 13.7% more than the average score and relaxed F1-score of 35% in ADR extraction + normalization of task 3 which is 5.8% more than the average score. Overall, our models achieve promising results in both the tasks with significant improvements over average scores.

## introduction social media platforms in particular, twitter are used extensively by common public to share their experiences which also includes health-related information like adverse drug reaction (adr) they experience while consuming drugs. adverse drug reaction refers to unwanted harmful effect following the use of one or more drugs. the abundant health-related social media data can be utilized to enhance the quality of services in health-related applications @xcite . our team participated in task 2 and task 3 of smm4h 2020 shared task @xcite . task 2 aims at identifying whether a tweet contains adr mention or not. task 3 aims at extracting adr mentions and then normalizing them to meddra concepts. ## conclusion in this work, we explored the effectiveness of roberta to identify, extract and normalize adr mentions in tweets. in both task 2 and task 3, our proposed models achieved promising results with significant improvements over average scores.