A classification algorithm to predict chronic pain using both regression and machine learning – A stepwise approach.
This secondary data analysis study aimed to (1) investigate the use of two sense-based parameters (movement and sleep hours) as predictors of chronic pain when controlling for patient demographics and depression, and (2) identify a classification model with accuracy in predicting chronic pain. Data...
| Published in: | Applied Nursing Research Vol. 62 |
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| Main Authors: | , , , , , , , |
| Format: | research Journal Article |
| Published: |
Elsevier B.V.
Dec2021
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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=153682439&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153682439 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971897 44Z jtl: Applied Nursing Research issn: 08971897 maglogo: N pubinfo: dt: Dec2021 vid: 62 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 153682439 153682439 153682439 10.1016/j.apnr.2021.151504 153682439 ppct: 1 formats: tig: atl: A classification algorithm to predict chronic pain using both regression and machine learning – A stepwise approach. aug: au: Tsai, Pao-Feng Wang, Chih-Hsuan Zhou, Yang Ren, Jiaxiang Jones, Alisha Watts, Sarah O. Chou, Chiahung Ku, Wei-Shinn affil: School of Nursing, Auburn University, Auburn, AL 36849, United States of America sug: subj: Classification Algorithms Chronic Pain Risk Factors Risk Assessment Machine Learning Depression Prevention and Control Sensitivity and Specificity Human Step-Wise Multiple Regression Secondary Analysis Questionnaires Community Living In Old Age Neural Networks (Computer) Step Descriptive Statistics Prediction Models Aged Movement Sleep Aged: 65+ years ab: This secondary data analysis study aimed to (1) investigate the use of two sense-based parameters (movement and sleep hours) as predictors of chronic pain when controlling for patient demographics and depression, and (2) identify a classification model with accuracy in predicting chronic pain. Data collected by Oregon Health & Science University between March 2018 and December 2019 under the Collaborative Aging Research Using Technology Initiative were analyzed in two stages. Data were collected by sensor technologies and questionnaires from older adults living independently or with a partner in the community. In Stage 1, regression models were employed to determine unique sensor-based behavioral predictors of pain. These sensor-based parameters were used to create a classification model to predict the weekly recalled pain intensity and interference level using a deep neural network model, a machine learning approach, in Stage 2. Daily step count was a unique predictor for both pain intensity (75% Accuracy, F1 = 0.58) and pain interference (82% Accuracy, F1 = 0.59). The developed classification model performed well in this dataset with acceptable accuracy scores. This study demonstrated that machine learning technique can be used to identify the relationship between patients' pain and the risk factors. • Chronic pain disproportionally affects older adults, but their pains are not adequately treated. • Existing non-verbal tools or advanced sensor technologies for pain detection are not feasible in community clinics. • Daily step count is a unique predictor for levels of pain, but additional parameters are needed to improve the prediction. • Combining statistical and machine learning approaches provides a powerful method to develop a predictive model. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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