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


ABSTRACT: 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?

SUBMITTER: Liew BXW 

PROVIDER: S-EPMC7545179 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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

Liew Bernard X W BXW   Peolsson Anneli A   Rugamer David D   Wibault Johanna J   Löfgren Hakan H   Dedering Asa A   Zsigmond Peter P   Falla Deborah D  

Scientific reports 20201008 1


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 adaptiv  ...[more]

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