This paper outlines the performance evaluation of a system for adverse drug event normalization, developed by the Data Science for Digital Health group for the Social Media Mining for Health Applications 2023 shared task 5. Shared task 5 targeted the normalization of adverse drug event mentions in Twitter to standard concepts from the Medical Dictionary for Regulatory Activities terminology. Our system hinges on a two-stage approach: BERT fine-tuning for entity recognition, followed by zero-shot normalization using sentence transformers and reciprocal-rank fusion. The approach yielded a precision of 44.9%, recall of 40.5%, and an F1-score of 42.6%. It outperformed the median performance in shared task 5 by 10% and demonstrated the highest performance among all participants. These results substantiate the effectiveness of our approach and its potential application for adverse drug event normalization in the realm of social media text mining.

通过BERT精调实体识别和句子转换器以及互惠排序融合，本研究提出的系统在社交媒体文本挖掘领域中，对不良药物事件标准化取得了高效性能表现，实现了44.9%的准确率、40.5%的召回率和42.6%的F1分数，在共享任务5中表现优于中位数10%且超过了其他参与者的性能。

DS4DH在＃SMM4H 2023中：使用句向量和倒数排名融合进行零射击药物不良事件规范化