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...
| Publicado en: | Chiropractic & Manual Therapies Vol. 28; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BioMed Central
7/17/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144640515&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144640515 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2045709X BG1Z jtl: Chiropractic & Manual Therapies issn: 2045709X maglogo: N pubinfo: dt: 7/17/2020 vid: 28 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 144640515 144640515 144640515 10.1186/s12998-020-00335-4 144640515 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization. aug: au: Coleman, Brian C. Fodeh, Samah Lisi, Anthony J. Goulet, Joseph L. Corcoran, Kelsey L. Bathulapalli, Harini Brandt, Cynthia A. affil: Pain Research, Informatics, Multimorbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, 11-ACSL-G, 950 Campbell Avenue, 06516, West Haven, CT, USA sug: subj: Back Pain Therapy Chronic Pain Therapy Veterans Health Services Utilization Chiropractic Utilization Prediction Models Evaluation Machine Learning Methods Human Male Female Adult Middle Age Veterans United States Department of Veterans Affairs Retrospective Design Prospective Studies Usability Study Models, Statistical Evaluation Algorithms Evaluation Neural Networks (Computer) Predictive Validity Evaluation Analysis of Variance Chi Square Test Factor Analysis Descriptive Statistics Confidence Intervals Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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