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

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Title:Using Explainable Deep Learning and Logistic Regression to Evaluate Complementary and Integrative Health Treatments in Patients with Musculoskeletal Disorders
Authors:Redd, Douglas
Goulet, Joseph
Zeng-Treitler, Qing
Keywords:Big Data on Healthcare Application
complementary and integrative health
deep learning
explainable artificial intelligence
logistic regression
Date Issued:07 Jan 2020
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.
Pages/Duration:9 pages
URI:http://hdl.handle.net/10125/64140
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.398
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Big Data on Healthcare Application


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