Using Explainable Deep Learning and Logistic Regression to Evaluate Complementary and Integrative Health Treatments in Patients with Musculoskeletal Disorders
Using Explainable Deep Learning and Logistic Regression to Evaluate Complementary and Integrative Health Treatments in Patients with Musculoskeletal Disorders
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Date
2020-01-07
Authors
Redd, Douglas
Goulet, Joseph
Zeng-Treitler, Qing
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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.
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Keywords
Big Data on Healthcare Application,
complementary and integrative health,
deep learning,
explainable artificial intelligence,
logistic regression
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9 pages
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Proceedings of the 53rd Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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