Identifying Adverse Drug Events from Social Media using an Improved Semi-Supervised Method

Adverse drug event (ADE) is a serious health concern. Social media has provided patients a broad platform to share their ADE experiences, impelling the development of social media-based pharmacovigilance. However, social media analysis of ADEs presents several important challenges that need to be addressed for high-performing ADE identification. To address these challenges, a feature Weighted-based Improved Disagreement-based Semi-Supervised Learning method, named WIDSSL, is proposed for effectively identifying ADEs from non-ADEs. Empirical results demonstrate the effectiveness of WIDSSL. Our proposed WIDSSL method can reduce the reliance on a large number of labeled instances for high-performing ADE identification, and hence enhance the feasibility of conducting social media-based pharmacovigilance.