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    <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.</abstract>
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%0 Conference Proceedings
%T CSECU-DSG@SMM4H’22: Transformer based Unified Approach for Classification of Changes in Medication Treatments in Tweets and WebMD Reviews
%A Sultana, Afrin
%A Chowdhury, Nihad Karim
%A Chy, Abu Nowshed
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F sultana-etal-2022-csecu
%X 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.
%U https://aclanthology.org/2022.smm4h-1.33
%P 118-122

Markdown (Informal)

[CSECU-DSG@SMM4H’22: Transformer based Unified Approach for Classification of Changes in Medication Treatments in Tweets and WebMD Reviews](https://aclanthology.org/2022.smm4h-1.33) (Sultana et al., SMM4H 2022)

• 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)

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.