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In this study, we introduce a data-driven framework, adapt and integrate multiple advanced machine learning techniques, and perform an empirical analysis to (1) derive characteristics of self-reported asthma patients from social media, (2) enable integration and repurposing of highly heterogeneous and commonly available datasets, and (3) uncover the sequential patterns of asthma triggers and risk factors, and their relative importance, both of which are difficult to achieve via retrospective cohort-based studies. Our methods and results can provide guidance for developing asthma management plans and interventions for specific subpopulations and, eventually, have the potential to reduce the societal burden of asthma.<\/jats:p>","DOI":"10.25300\/misq\/2020\/15106","type":"journal-article","created":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T16:13:37Z","timestamp":1583338417000},"page":"305-350","source":"Crossref","is-referenced-by-count":53,"title":["A Comprehensive Analysis of Triggers and Risk Factors for Asthma Based on Machine Learning and Large Heterogeneous Data Sources"],"prefix":"10.25300","volume":"44","author":[{"given":"Wenli","family":"Zhang","sequence":"first","affiliation":[{"name":"Debbie and Jerry Ivy College of Business, Iowa State University, 2167 Union Drive, Ames, IA 50011 U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudha","family":"Ram","sequence":"additional","affiliation":[{"name":"Eller College of Management, University of Arizona, 1130 E. 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