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.

中文翻译：

---

从社交媒体了解药物不依从性：一种情感丰富的深度学习方法

药物不依从性 (MNA) 会导致严重的健康后果，并使美国医疗保健系统每年损失 2900 亿美元。了解患者的 MNA 原因是研究人员、从业人员和制药行业减轻这些健康和经济后果的紧迫目标。过去几年，患者对社交媒体的参与度飙升，使其成为一种具有成本效益且丰富的信息来源，可以补充先前的调查研究并加深对 MNA 的理解。然而，由于技术挑战，例如长文本中的负面决策、不同的患者词汇和稀疏的相关信息，这样的数据集在现有的 MNA 研究中尚未开发。在这项工作中，我们开发了情感丰富的深度学习 (SEDEL) 来应对这些挑战并提取 MNA 原因。我们在 53 上评估 SEDEL，约180种药物180条review，准确率89.25%，召回率88.48%，F1分数88.86%。SEDEL 明显优于最先进的基线模型。领域专家识别并验证了九类 MNA 原因。本研究在两个方面对信息系统研究做出了贡献。首先，我们设计了一种新颖的基于深度学习的推理挖掘方法。其次，我们的结果为卫生行业和从业人员设计干预措施提供了直接影响。我们设计了一种新颖的基于深度学习的推理挖掘方法。其次，我们的结果为卫生行业和从业人员设计干预措施提供了直接影响。我们设计了一种新颖的基于深度学习的推理挖掘方法。其次，我们的结果为卫生行业和从业人员设计干预措施提供了直接影响。