Tyck till om SwePub Sök
här!
Sökning: onr:"swepub:oai:DiVA.org:liu-170931" >
Clinical predictive...
Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy : a machine learning approach
-
- Liew, Bernard X. W. (författare)
- Univ Essex, England
-
- Peolsson, Anneli (författare)
- Linköpings universitet,Avdelningen för prevention, rehabilitering och nära vård,Medicinska fakulteten
-
- Rugamer, David (författare)
- Ludwig Maximilians Univ Munchen, Germany; Humboldt Univ, Germany
-
visa fler...
-
- Wibault, Johanna (författare)
- Linköpings universitet,Avdelningen för prevention, rehabilitering och nära vård,Medicinska fakulteten,Region Östergötland, Rörelse och Hälsa
-
- Löfgren, Håkan (författare)
- Linköpings universitet,Avdelningen för kirurgi, ortopedi och onkologi,Medicinska fakulteten,Neuroorthoped Ctr, Sweden
-
- Dedering, Asa (författare)
- Karolinska Institutet,Karolinska Univ Hosp, Sweden; Karolinska Inst, Sweden
-
- Zsigmond, Peter, 1966- (författare)
- Linköpings universitet,Avdelningen för kirurgi, ortopedi och onkologi,Medicinska fakulteten,Region Östergötland, Neurokirurgiska kliniken US
-
- Falla, Deborah (författare)
- Univ Birmingham, England
-
visa färre...
-
(creator_code:org_t)
- 2020-10-08
- 2020
- Engelska.
-
Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 10:1
- Relaterad länk:
-
https://liu.diva-por... (primary) (Raw object)
-
visa fler...
-
https://www.nature.c...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
http://kipublication...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Sjukgymnastik (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Physiotherapy (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas