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Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach

Liew, Bernard XW and Peolsson, Anneli and Rugamer, David and Wibault, Johanna and Löfgren, Hakan and Dedering, Asa and Zsigmond, Peter and Falla, Deborah (2020) 'Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach.' Scientific Reports, 10 (1). 16782-. ISSN 2045-2322

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Prognostic models play an important role in the clinical management of cervical radiculopathy (CR). No study has compared the performance of modern machine learning techniques, against more traditional stepwise regression techniques, when developing prognostic models in individuals with CR. We analysed a prospective cohort dataset of 201 individuals with CR. Four modelling techniques (stepwise regression, least absolute shrinkage and selection operator [LASSO], boosting, and multivariate adaptive regression splines [MuARS]) were each used to form a prognostic model for each of four outcomes obtained at a 12 month follow-up (disability—neck disability index [NDI]), quality of life (EQ5D), present neck pain intensity, and present arm pain intensity). For all four outcomes, the differences in mean performance between all four models were small (difference of NDI < 1 point; EQ5D < 0.1 point; neck and arm pain < 2 points). Given that the predictive accuracy of all four modelling methods were clinically similar, the optimal modelling method may be selected based on the parsimony of predictors. Some of the most parsimonious models were achieved using MuARS, a non-linear technique. Modern machine learning methods may be used to probe relationships along different regions of the predictor space.

Item Type: Article
Uncontrolled Keywords: Cervical Vertebrae; Humans; Neck Pain; Radiculopathy; Prognosis; Orthopedic Procedures; Postoperative Period; Prospective Studies; Recovery of Function; Models, Theoretical; Adult; Middle Aged; Female; Male; Machine Learning
Divisions: Faculty of Science and Health
Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Nov 2020 14:56
Last Modified: 18 Aug 2022 12:20

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