A Comprehensive Analysis of Triggers and Risk Factors for Asthma Based on Machine Learning and Large Heterogeneous Data Sources
AffiliationUniv Arizona, Eller Coll Management
KeywordsChronic disease management
asthma triggers/risk factors
convolutional neural networks
sequential pattern mining
MetadataShow full item record
PublisherSOC INFORM MANAGE-MIS RES CENT
CitationWenli Zhang, & Ram, S. (2020). A Comprehensive Analysis of Triggers and Risk Factors for Asthma Based on Machine Learning and Large Heterogeneous Data Sources. MIS Quarterly, 44(1), 305–349.
RightsCopyright © 2019 by the Management Information Systems Research Center (MISRC) of the University of Minnesota.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractAsthma is a common chronic health condition affecting millions of people in the United States. While asthma cannot be cured, it can be managed if we identify and understand triggers and risk factors that cause asthma exacerbations. However, this is challenging because these triggers and risk factors are complex and interconnected, and there are limitations to current mainstream approaches for identifying them. The recent availability of massive amounts of heterogeneous data has opened up new possibilities for asthma triggers and risk factors analyses. 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 selfreported 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.
Note60 month embargo; published 01 March 2020
VersionFinal published version