Predictive modelling of hospital-acquired infection in acute ischemic stroke using machine learning.

ABSTRACT: Hospital-acquired infections (HAIs) are serious complication for patients with acute ischemic stroke (AIS), often resulting in poor functional outcomes. However, no existing model can specifically predict HAI in AIS patients. Therefore, we employed the Gradient Boosting matching learning algorithm to establish predictive models for HAI occurrence in AIS patients and poor 30-day functional outcomes (modified Rankin Scale > 2) in AIS patients with HAI by analyzing electronic health records from 6560 AIS patients. Model performance was evaluated through internal cross-validation and external validation using an independent cohort of 3521 AIS patients. The established models demonstrated robust predictive performance for HAI in AIS patients, achieving area under the receiver operating characte

SUBMITTER: Chang CW 

PROVIDER: S-EPMC11680783 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature