UNDERSTANDING MEDICATION NONADHERENCE FROM SOCIAL MEDIA: A SENTIMENT-ENRICHED DEEP LEARNING APPROACH br
Xie, Jiaheng1; Liu, Xiao2; Zeng, Daniel Dajun3,4; Fang, Xiao1
Source PublicationMIS QUARTERLY
ISSN0276-7783
2022-03-01
Volume46Issue:1Pages:341-372
Corresponding AuthorXie, Jiaheng(jxie@udel.edu)
AbstractMedication 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
KeywordSentiment-enriched deep learning reason mining social media analytics health risk analytics medication nonadherence
DOI10.25300/MISQ/2022/15336
WOS KeywordBIG DATA ; DESIGN SCIENCE ; ADHERENCE ; ANALYTICS ; WORD ; PERSPECTIVE ; EXTRACTION ; FRAMEWORK ; FEATURES ; SUPPORT
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS Research AreaComputer Science ; Information Science & Library Science ; Business & Economics
WOS SubjectComputer Science, Information Systems ; Information Science & Library Science ; Management
WOS IDWOS:000785872600009
PublisherSOC INFORM MANAGE-MIS RES CENT
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48397
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorXie, Jiaheng
Affiliation1.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
Recommended Citation
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.
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