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

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Liew, Bernard X. W.Univ Essex, England (author)

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

Article/chapterEnglish2020

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2020-10-08
Nature Publishing Group,2020
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LIBRIS-ID:oai:DiVA.org:liu-170931
https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170931URI
https://doi.org/10.1038/s41598-020-73740-7DOI
http://kipublications.ki.se/Default.aspx?queryparsed=id:144878767URI

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Language:English
Summary in:English

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Funding Agencies|Swedish Research CouncilSwedish Research Council; Swedish Society of Medicine; Medical Research Council of Southeast Sweden; Region Ostergotland; Lions; Futurum (Academy of Health and Care, Region Jonkoping County)
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.

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Peolsson, AnneliLinköpings universitet,Avdelningen för prevention, rehabilitering och nära vård,Medicinska fakulteten(Swepub:liu)annpe35 (author)
Rugamer, DavidLudwig Maximilians Univ Munchen, Germany; Humboldt Univ, Germany (author)
Wibault, JohannaLinköpings universitet,Avdelningen för prevention, rehabilitering och nära vård,Medicinska fakulteten,Region Östergötland, Rörelse och Hälsa(Swepub:liu)johwi23 (author)
Löfgren, HåkanLinköpings universitet,Avdelningen för kirurgi, ortopedi och onkologi,Medicinska fakulteten,Neuroorthoped Ctr, Sweden(Swepub:liu)haklo96 (author)
Dedering, AsaKarolinska Institutet,Karolinska Univ Hosp, Sweden; Karolinska Inst, Sweden (author)
Zsigmond, Peter,1966-Linköpings universitet,Avdelningen för kirurgi, ortopedi och onkologi,Medicinska fakulteten,Region Östergötland, Neurokirurgiska kliniken US(Swepub:liu)petzs86 (author)
Falla, DeborahUniv Birmingham, England (author)
Univ Essex, EnglandAvdelningen för prevention, rehabilitering och nära vård (creator_code:org_t)

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In:Scientific Reports: Nature Publishing Group10:12045-2322

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