Profiling Arthritis Pain with a Decision Tree.
Abstract: Background: Arthritis is the leading cause of work disability and contributes to lost productivity. Previous studies showed that various factors predict pain, but they were limited in sample size and scope from a data analytics perspective. Objectives: The current study applied machine lea...
| Published in: | Pain Practice Vol. 18; no. 5; pp. 568 - 580 |
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| Main Authors: | , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Jun2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129955920&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129955920 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15307085 HYN jtl: Pain Practice issn: 15307085 maglogo: Y pubinfo: dt: Jun2018 vid: 18 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 129955920 129955920 129955920 10.1111/papr.12645 129955920 ppf: 568 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Profiling Arthritis Pain with a Decision Tree. aug: au: Hung, Man Bounsanga, Jerry Liu, Fangzhou Voss, Maren W. affil: Department of Orthopaedics, University of Utah, Salt Lake City, Utah, U.S.A. sug: subj: Arthritis Complications Pain Etiology Decision Trees Machine Learning Algorithms Human Surveys Data Mining Billing and Claims Patient-Reported Outcomes Social Behavior Medical Records Data Analysis, Statistical Sensitivity and Specificity Functional Assessment Cost Benefit Analysis Pain Risk Factors Stair Climbing ab: Abstract: Background: Arthritis is the leading cause of work disability and contributes to lost productivity. Previous studies showed that various factors predict pain, but they were limited in sample size and scope from a data analytics perspective. Objectives: The current study applied machine learning algorithms to identify predictors of pain associated with arthritis in a large national sample. Methods: Using data from the 2011 to 2012 Medical Expenditure Panel Survey, data mining was performed to develop algorithms to identify factors and patterns that contribute to risk of pain. The model incorporated over 200 variables within the algorithm development, including demographic data, medical claims, laboratory tests, patient‐reported outcomes, and sociobehavioral characteristics. Results: The developed algorithms to predict pain utilize variables readily available in patient medical records. Using the machine learning classification algorithm J48 with 50‐fold cross‐validations, we found that the model can significantly distinguish those with and without pain (c‐statistics = 0.9108). The F measure was 0.856, accuracy rate was 85.68%, sensitivity was 0.862, specificity was 0.852, and precision was 0.849. Conclusion: Physical and mental function scores, the ability to climb stairs, and overall assessment of feeling were the most discriminative predictors from the 12 identified variables, predicting pain with 86% accuracy for individuals with arthritis. In this era of rapid expansion of big data application, the nature of healthcare research is moving from hypothesis‐driven to data‐driven solutions. The algorithms generated in this study offer new insights on individualized pain prediction, allowing the development of cost‐effective care management programs for those experiencing arthritis pain. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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