@inproceedings{sarrouti-etal-2023-exploring,
    title = "Exploring Drug Switching in Patients: A Deep Learning-based Approach to Extract Drug Changes and Reasons from Social Media",
    author = "Sarrouti, Mourad  and
      Tao, Carson  and
      Mamy Randriamihaja, Yoann",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.10",
    doi = "10.18653/v1/2023.bionlp-1.10",
    pages = "127--132",
    abstract = "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.",
}

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.
Sarrouti, Mourad Tao, Carson Mamy R riamihaja, Yoann
Exploring Drug Switching in Patients: A Deep Learning-based Approach to Extract Drug Changes and Reasons from Social Media