Machine learning to predict mortality after rehabilitation among patients with severe stroke.

ABSTRACT: Stroke is among the leading causes of death and disability worldwide. Approximately 20-25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. A

SUBMITTER: Scrutinio D 

PROVIDER: S-EPMC7674405 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature