Understanding Medication Nonadherence from Social Media: A Sentiment-Enriched Deep Learning Approach
开发了一种情感增强的深度学习模型，从53,180条药物评论中提取出九类药物不依从原因，准确率达89.25%，为医疗干预设计提供依据。
Medication nonadherence (MNA) can lead to serious health ramifications and costs U.S. healthcare systems $290 billion annually. Understanding the reasons underlying patients’ MNA is thus an urgent goal for researchers, practitioners, and the pharmaceutical industry in order to mitigate negative health and economic consequences. In recent years, patient engagement on social media sites has soared, making it a cost-efficient and rich information source that can complement prior survey studies and deepen the understanding of MNA. Yet these data remain untapped in existing MNA studies because of technical challenges such as long texts, decision-making based on negative sentiment, varied patient vocabulary, and the scarcity of relevant information. For this study, we developed a sentiment-enriched deep learning method (SEDEL) to address these challenges and extract reasons for MNA. We evaluated SEDEL using 53,180 reviews concerning 180 drugs and achieved a precision of 89.25%, a recall of 88.48%, and an F1 score of 88.86%. SEDEL significantly outperformed state-of-the-art baseline models. We identified nine categories of MNA reasons, which were verified by domain experts. This study contributes to IS research by devising a novel deep-learning-based approach for reason mining and by providing direct implications for the health industry and for practitioners regarding the design of interventions.