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

Simple item page
dc.contributor.author Redd, Douglas
dc.contributor.author Goulet, Joseph
dc.contributor.author Zeng-Treitler, Qing
dc.date.accessioned 2020-01-04T07:49:39Z
dc.date.available 2020-01-04T07:49:39Z
dc.date.issued 2020-01-07
dc.description.abstract There 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.extent 9 pages
dc.identifier.doi 10.24251/HICSS.2020.398
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/64140
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Big Data on Healthcare Application
dc.subject complementary and integrative health
dc.subject deep learning
dc.subject explainable artificial intelligence
dc.subject logistic regression
dc.title Using Explainable Deep Learning and Logistic Regression to Evaluate Complementary and Integrative Health Treatments in Patients with Musculoskeletal Disorders
dc.type Conference Paper
dc.type.dcmi Text
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
0322.pdf
Size:
321.93 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Big Data on Healthcare Application