2023/08/15 by Anthony Yazdani, Hossein Rouhizadeh, Yazdani, Anthony +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Social Sciences · #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Misinformation and Its Impacts #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2308.12877
openalex publication_date 2023/08/15 · openalex created_date 2023/08/26 · openalex updated_date 2026/07/28
This paper outlines the performance evaluation of a system for adverse drug event normalization, developed by the Data Science for Digital Health (DS4DH) group for the Social Media Mining for Health Applications (SMM4H) 2023 shared task 5. Shared task 5 targeted the normalization of adverse drug event mentions in Twitter to standard concepts of 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.