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.- Anthology ID:
- 2022.smm4h-1.33
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 118–122
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.smm4h-1.33/
- DOI:
- Cite (ACL):
- Afrin Sultana, Nihad Karim Chowdhury, and Abu Nowshed Chy. 2022. CSECU-DSG@SMM4H’22: Transformer based Unified Approach for Classification of Changes in Medication Treatments in Tweets and WebMD Reviews. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 118–122, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- CSECU-DSG@SMM4H’22: Transformer based Unified Approach for Classification of Changes in Medication Treatments in Tweets and WebMD Reviews (Sultana et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.smm4h-1.33.pdf