TY - JOUR
T1 - Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy
T2 - a machine learning approach
AU - Liew, Bernard X W
AU - Peolsson, Anneli
AU - Rugamer, David
AU - Wibault, Johanna
AU - Löfgren, Hakan
AU - Dedering, Asa
AU - Zsigmond, Peter
AU - Falla, Deborah
PY - 2020/10/8
Y1 - 2020/10/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85092286131&partnerID=8YFLogxK
U2 - 10.1038/s41598-020-73740-7
DO - 10.1038/s41598-020-73740-7
M3 - Article
C2 - 33033308
SN - 2045-2322
VL - 10
SP - 16782
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 16782
ER -