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UNDERSTANDING MEDICATION NONADHERENCE FROM SOCIAL MEDIA: A SENTIMENT-ENRICHED DEEP LEARNING APPROACH br

文献类型：期刊论文

作者Xie, Jiaheng2; Liu, Xiao3; Zeng, Daniel Dajun1,4; Fang, Xiao2
刊名MIS QUARTERLY
出版日期2022-03-01
卷号46期号:1页码:341-372
ISSN号0276-7783
关键词Sentiment-enriched deep learning reason mining social media analytics health risk analytics medication nonadherence
DOI10.25300/MISQ/2022/15336
通讯作者Xie, Jiaheng(jxie@udel.edu)
英文摘要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 pharmaceuticalindustry 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 concerning180 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
WOS关键词BIG DATA ; DESIGN SCIENCE ; ADHERENCE ; ANALYTICS ; WORD ; PERSPECTIVE ; EXTRACTION ; FRAMEWORK ; FEATURES ; SUPPORT
资助项目National Key Research and Development Program of China[2020AAA0103405] ; National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[62071467] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27030100]
WOS研究方向Computer Science ; Information Science & Library Science ; Business & Economics
语种英语
出版者SOC INFORM MANAGE-MIS RES CENT
WOS记录号WOS:000785872600009
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/48396]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Xie, Jiaheng
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Univ Delaware, Dept Accounting & Management Informat Syst, Lerner Coll Business & Econ, Newark, DE 19716 USA
3.Arizona State Univ, Dept Informat Syst, WP Carey Sch Business, Tempe, AZ 85287 USA
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xie, Jiaheng,Liu, Xiao,Zeng, Daniel Dajun,et al. UNDERSTANDING MEDICATION NONADHERENCE FROM SOCIAL MEDIA: A SENTIMENT-ENRICHED DEEP LEARNING APPROACH br[J]. MIS QUARTERLY,2022,46(1):341-372.
APA Xie, Jiaheng,Liu, Xiao,Zeng, Daniel Dajun,&Fang, Xiao.(2022).UNDERSTANDING MEDICATION NONADHERENCE FROM SOCIAL MEDIA: A SENTIMENT-ENRICHED DEEP LEARNING APPROACH br.MIS QUARTERLY,46(1),341-372.
MLA Xie, Jiaheng,et al."UNDERSTANDING MEDICATION NONADHERENCE FROM SOCIAL MEDIA: A SENTIMENT-ENRICHED DEEP LEARNING APPROACH br".MIS QUARTERLY 46.1(2022):341-372.

入库方式： OAI收割

来源：自动化研究所

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