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This paper outlines the performance evaluation of a system for adverse drugevent normalization, developed by the Data Science for Digital Health (DS4DH)group for the Social Media Mining for Health Applications (SMM4H) 2023 sharedtask 5. Shared task 5 targeted the normalization of adverse drug event mentionsin Twitter to standard concepts of the Medical Dictionary for RegulatoryActivities terminology. Our system hinges on a two-stage approach: BERTfine-tuning for entity recognition, followed by zero-shot normalization usingsentence transformers and reciprocal-rank fusion. The approach yielded aprecision of 44.9%, recall of 40.5%, and an F1-score of 42.6%. It outperformedthe median performance in shared task 5 by 10% and demonstrated the highestperformance among all participants. These results substantiate theeffectiveness of our approach and its potential application for adverse drugevent normalization in the realm of social media text mining.

DS4DH at #SMM4H 2023: Zero-Shot Adverse Drug Events Normalization using Sentence Transformers and Reciprocal-Rank Fusion