Understanding Medication Nonadherence from Social Media: A Sentiment-Enriched Deep Learning Approach

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Publication History

Received: December 21, 2017
Revised: October 25, 2018; December 18, 2019; June 24, 2020; November 24, 2020
Accepted: January 25, 2021
Published online: February 25, 2022


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Medication nonadherence (MNA) causes severe health ramifications and costs the U.S. healthcare systems $290 billion annually. Understanding patients’ MNA reasons is an urgent goal for researchers, practitioners, and the pharmaceutical industry to mitigate those health and economic consequences. Past years have witnessed soaring patient engagement in social media, making it a cost-efficient and rich information source that can complement prior survey studies and deepen the understanding of MNA. Yet, such a dataset is untapped in existing MNA studies due to technical challenges such as negative decision-making in long texts, varied patient vocabulary, and sparse relevant information. In this work, we develop Sentiment-Enriched DEep Learning (SEDEL) to address these challenges and extract MNA reasons. We evaluate SEDEL on 53,180 reviews of about 180 drugs and achieve a precision of 89.25%, a recall of 88.48%, and an F1 score of 88.86%. SEDEL significantly outperforms the state-of-the-art baseline models. Nine categories of MNA reasons are identified and verified by domain experts. This study contributes to IS research in two aspects. First, we devise a novel deep-learning-based approach for reason mining. Second, our results provide direct implications for the health industry and practitioners to design interventions.

Additional Details
Author Jiaheng Xie, Xiao Liu, Daniel Dajun Zeng, and Xiao Fang
Year 2022
Volume 46
Issue 1
Keywords Sentiment-enriched deep learning, reason mining, social media analytics, health risk analytics, medication nonadherence
Page Numbers 341-372; DOI: 10.25300/MISQ/2022/15336
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