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Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization

BACKGROUND: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain sta...

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Autores principales: Coleman, Brian C., Fodeh, Samah, Lisi, Anthony J., Goulet, Joseph L., Corcoran, Kelsey L., Bathulapalli, Harini, Brandt, Cynthia A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368704/
https://www.ncbi.nlm.nih.gov/pubmed/32680545
http://dx.doi.org/10.1186/s12998-020-00335-4
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author Coleman, Brian C.
Fodeh, Samah
Lisi, Anthony J.
Goulet, Joseph L.
Corcoran, Kelsey L.
Bathulapalli, Harini
Brandt, Cynthia A.
author_facet Coleman, Brian C.
Fodeh, Samah
Lisi, Anthony J.
Goulet, Joseph L.
Corcoran, Kelsey L.
Bathulapalli, Harini
Brandt, Cynthia A.
author_sort Coleman, Brian C.
collection PubMed
description BACKGROUND: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain status in different clinical populations, including veterans. Little is currently known about predicting healthcare service utilization in the domain of conservative interventions for spinal pain conditions, including the frequency of use of chiropractic services. The purpose of this retrospective cohort study was to explore the use of supervised machine learning approaches to predicting one-year chiropractic service utilization by veterans receiving VA chiropractic care. METHODS: We included 19,946 veterans who entered the Musculoskeletal Diagnosis Cohort between October 1, 2003 and September 30, 2013 and utilized VA chiropractic services within one year of cohort entry. The primary outcome was one-year chiropractic service utilization following index chiropractic visit, split into quartiles represented by the following classes: 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or greater visits. We compared the performance of four multiclass classification algorithms (gradient boosted classifier, stochastic gradient descent classifier, support vector classifier, and artificial neural network) in predicting visit quartile using 158 sociodemographic and clinical features. RESULTS: The selected algorithms demonstrated poor prediction capabilities. Subset accuracy was 42.1% for the gradient boosted classifier, 38.6% for the stochastic gradient descent classifier, 41.4% for the support vector classifier, and 40.3% for the artificial neural network. The micro-averaged area under the precision-recall curve for each one-versus-rest classifier was 0.43 for the gradient boosted classifier, 0.38 for the stochastic gradient descent classifier, 0.43 for the support vector classifier, and 0.42 for the artificial neural network. Performance of each model yielded only a small positive shift in prediction probability (approximately 15%) compared to naïve classification. CONCLUSIONS: Using supervised machine learning to predict chiropractic service utilization remains challenging, with only a small shift in predictive probability over naïve classification and limited clinical utility. Future work should examine mechanisms to improve model performance.
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spelling pubmed-73687042020-07-20 Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization Coleman, Brian C. Fodeh, Samah Lisi, Anthony J. Goulet, Joseph L. Corcoran, Kelsey L. Bathulapalli, Harini Brandt, Cynthia A. Chiropr Man Therap Research BACKGROUND: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain status in different clinical populations, including veterans. Little is currently known about predicting healthcare service utilization in the domain of conservative interventions for spinal pain conditions, including the frequency of use of chiropractic services. The purpose of this retrospective cohort study was to explore the use of supervised machine learning approaches to predicting one-year chiropractic service utilization by veterans receiving VA chiropractic care. METHODS: We included 19,946 veterans who entered the Musculoskeletal Diagnosis Cohort between October 1, 2003 and September 30, 2013 and utilized VA chiropractic services within one year of cohort entry. The primary outcome was one-year chiropractic service utilization following index chiropractic visit, split into quartiles represented by the following classes: 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or greater visits. We compared the performance of four multiclass classification algorithms (gradient boosted classifier, stochastic gradient descent classifier, support vector classifier, and artificial neural network) in predicting visit quartile using 158 sociodemographic and clinical features. RESULTS: The selected algorithms demonstrated poor prediction capabilities. Subset accuracy was 42.1% for the gradient boosted classifier, 38.6% for the stochastic gradient descent classifier, 41.4% for the support vector classifier, and 40.3% for the artificial neural network. The micro-averaged area under the precision-recall curve for each one-versus-rest classifier was 0.43 for the gradient boosted classifier, 0.38 for the stochastic gradient descent classifier, 0.43 for the support vector classifier, and 0.42 for the artificial neural network. Performance of each model yielded only a small positive shift in prediction probability (approximately 15%) compared to naïve classification. CONCLUSIONS: Using supervised machine learning to predict chiropractic service utilization remains challenging, with only a small shift in predictive probability over naïve classification and limited clinical utility. Future work should examine mechanisms to improve model performance. BioMed Central 2020-07-17 /pmc/articles/PMC7368704/ /pubmed/32680545 http://dx.doi.org/10.1186/s12998-020-00335-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Coleman, Brian C.
Fodeh, Samah
Lisi, Anthony J.
Goulet, Joseph L.
Corcoran, Kelsey L.
Bathulapalli, Harini
Brandt, Cynthia A.
Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization
title Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization
title_full Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization
title_fullStr Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization
title_full_unstemmed Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization
title_short Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization
title_sort exploring supervised machine learning approaches to predicting veterans health administration chiropractic service utilization
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368704/
https://www.ncbi.nlm.nih.gov/pubmed/32680545
http://dx.doi.org/10.1186/s12998-020-00335-4
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