@inproceedings{sultana-etal-2022-csecu,
    title = "{CSECU}-{DSG}@{SMM}4{H}{'}22: Transformer based Unified Approach for Classification of Changes in Medication Treatments in Tweets and {W}eb{MD} Reviews",
    author = "Sultana, Afrin  and
      Chowdhury, Nihad Karim  and
      Chy, Abu Nowshed",
    editor = "Gonzalez-Hernandez, Graciela  and
      Weissenbacher, Davy",
    booktitle = "Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.smm4h-1.33/",
    pages = "118--122",
    abstract = "Medications play a vital role in medical treatment as medication non-adherence reduces clinical benefit, results in morbidity, and medication wastage. Self-declared changes in drug treatment and their reasons are automatically extracted from tweets and user reviews, helping to determine the effectiveness of drugs and improve treatment care. SMM4H 2022 Task 3 introduced a shared task focusing on the identification of non-persistent patients from tweets and WebMD reviews. In this paper, we present our participation in this task. We propose a neural approach that integrates the strengths of the transformer model, the Long Short-Term Memory (LSTM) model, and the fully connected layer into a unified architecture. Experimental results demonstrate the competitive performance of our system on test data with 61{\%} F1-score on task 3a and 86{\%} F1-score on task 3b. Our proposed neural approach ranked first in task 3b."
}