Samenvatting

Background
Accurate patient-specific predictions on return-to-work after traumatic brain injury (TBI) can support both clinical practice and policymaking. The use of machine learning on large administrative data provides interesting opportunities to create such prognostic models.

Aim
The current study assesses whether return-to-work one year after TBI can be predicted accurately from administrative data. Additionally, this study explores how model performance and feature importance change depending on whether a distinction is made between mild and moderate-to-severe TBI.

Methods
This study used a population-based dataset that combined discharge, claims and social security data of patients hospitalized with a TBI in Belgium during the year 2016. The prediction of TBI was attempted with three algorithms, elastic net logistic regression, random forest and gradient boosting and compared in their performance by their accuracy, sensitivity, specificity and area under the receiver operator curve (ROC AUC).

Results
The distinct modelling algorithms resulted in similar results, with 83% accuracy (ROC AUC 85%) for a binary classification of employed vs. not employed and up to 76% (ROC AUC 82%) for a multiclass operationalization of employment outcome. Modelling mild and moderate-to-severe TBI separately did not result in considerable differences in model performance and feature importance. The features of main importance for return-to-work prediction were related to pre-injury employment.

Discussion
While clearly offering some information beneficial for predicting return-to-work, administrative data needs to be supplemented with additional information to allow further improvement of patient-specific prognose.
Originele taal-2English
Artikelnummer105201
Aantal pagina's21
TijdschriftInternational Journal of Medical Informatics
Volume178
DOI's
StatusPublished - okt 2023

Bibliografische nota

Publisher Copyright:
© 2023 Elsevier B.V.

Vingerafdruk

Duik in de onderzoeksthema's van 'Predicting return to work after traumatic brain injury using machine learning and administrative data'. Samen vormen ze een unieke vingerafdruk.

Citeer dit