Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion.

ABSTRACT:

Objective

Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long-term functional independency.

Methods

Our study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0-2 at 90 days poststroke. Model performance was assessed using a 20-fold cross-validation and one-sided Wilcoxon rank-sum tests. Key featur

SUBMITTER: Shirvani O 

PROVIDER: S-EPMC11514938 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature