Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach.
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ABSTRACT:
SUBMITTER: Liew BXW
PROVIDER: S-EPMC7545179 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
Publications
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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