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

doi:10.25300/MISQ/2022/15336

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	UNDERSTANDING MEDICATION NONADHERENCE FROM SOCIAL MEDIA: A SENTIMENT-ENRICHED DEEP LEARNING APPROACH br
	Xie, Jiaheng 1; Liu, Xiao 2; Zeng, Daniel Dajun3,4 ; Fang, Xiao 1
发表期刊	MIS QUARTERLY
ISSN	0276-7783
	2022-03-01
卷号	46 期号:1 页码:341-372
通讯作者	Xie, Jiaheng(jxie@udel.edu)
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
关键词	Sentiment-enriched deep learning reason mining social media analytics health risk analytics medication nonadherence
DOI	10.25300/MISQ/2022/15336
关键词[WOS]	BIG DATA ; DESIGN SCIENCE ; ADHERENCE ; ANALYTICS ; WORD ; PERSPECTIVE ; EXTRACTION ; FRAMEWORK ; FEATURES ; SUPPORT
收录类别	SCI
语种	英语
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]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS研究方向	Computer Science ; Information Science & Library Science ; Business & Economics
WOS类目	Computer Science, Information Systems ; Information Science & Library Science ; Management
WOS记录号	WOS:000785872600009
出版者	SOC INFORM MANAGE-MIS RES CENT
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48397
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Xie, Jiaheng
作者单位	1.Univ Delaware, Dept Accounting & Management Informat Syst, Lerner Coll Business & Econ, Newark, DE 19716 USA 2.Arizona State Univ, Dept Informat Syst, WP Carey Sch Business, Tempe, AZ 85287 USA 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 4.Univ Chinese Acad Sci, 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.