Social media (SM) can provide valuable information about patients’ experiences with multiple drugs during treatments. Although information extraction from SM has been well-studied, drug switches detection and reasons behind these switches from SM have not been studied yet. Therefore, in this paper, we present a new SM listening approach for analyzing online patient conversations that contain information about drug switching, drug effectiveness, side effects, and adverse drug reactions. We describe a deep learning-based approach for identifying instances of drug switching in SM posts, as well as a method for extracting the reasons behind these switches. To train and test our models, we used annotated SM data from internal dataset which is automatically created using a rule-based method. We evaluated our models using Text-to-Text Transfer Transformer (T5) and found that our SM listening approach can extract medication change information and reasons with high accuracy, achieving an F1-score of 98% and a ROUGE-1 score of 93%, respectively. Overall, our results suggest that our SM listening approach has the potential to provide valuable insights into patients’ experiences with drug treatments, which can be used to improve patient outcomes and the effectiveness of drug treatments.

Biomedical relation extraction, aiming to automatically discover high-quality and semantic relations between the entities from free text, is becoming a vital step for automated knowledge discovery. Pretrained language models have achieved impressive performance on various natural language processing tasks, including relation extraction. In this paper, we perform extensive empirical comparisons of encoder-only transformers with the encoder-decoder transformer, specifically T5, on ten public biomedical relation extraction datasets. We study the relation extraction task from four major biomedical tasks, namely chemical-protein relation extraction, disease-protein relation extraction, drug-drug interaction, and protein-protein interaction. We also explore the use of multi-task fine-tuning to investigate the correlation among major biomedical relation extraction tasks. We report performance (micro F-score) using T5, BioBERT and PubMedBERT, demonstrating that T5 and multi-task learning can improve the performance of the biomedical relation extraction task.