Using Explainable Deep Learning and Logistic Regression to Evaluate Complementary and Integrative Health Treatments in Patients with Musculoskeletal Disorders

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dc.contributor.authorRedd, Douglas
dc.contributor.authorGoulet, Joseph
dc.contributor.authorZeng-Treitler, Qing
dc.date.accessioned2020-01-04T07:49:39Z
dc.date.available2020-01-04T07:49:39Z
dc.date.issued2020-01-07
dc.description.abstractThere is an increasing interest in the use of Complementary and Integrative Health (CIH) for treatment of pain as an alternative to opioid medications. We use a novel explainable deep learning approach compared and contrasted to a traditional logistic regression model to explore the impact of musculoskeletal disorder related factors on the use of CIH. The impact scores from the neural network show high correlation with the log-odds ratios of the logistic regression, showing the promise that neural networks can be used to identify high impact factors without depending on a priori assumptions and limitations of traditional statistical models.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2020.398
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64140
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data on Healthcare Application
dc.subjectcomplementary and integrative health
dc.subjectdeep learning
dc.subjectexplainable artificial intelligence
dc.subjectlogistic regression
dc.titleUsing Explainable Deep Learning and Logistic Regression to Evaluate Complementary and Integrative Health Treatments in Patients with Musculoskeletal Disorders
dc.typeConference Paper
dc.type.dcmiText

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