Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome.

ABSTRACT: Background: Distinguishing ICU patients with candidaemia can help with the precise prescription of antifungal drugs to create personalized guidelines. Previous prediction models of candidaemia have primarily used traditional logistic models and had some limitations. In this study, we developed a machine learning algorithm trained to predict candidaemia in patients with new-onset systemic inflammatory response syndrome (SIRS). Methods: This retrospective, observational study used clinical information collected between January 2013 and December 2017 from three hospitals. The ICU patient data were used to train 4 machine learning algorithms-XGBoost, Support Vector Machine (SVM), Random Forest (RF), ExtraTrees (ET)-and a logistic regression (LR) model to predict patients with can

SUBMITTER: Yuan S 

PROVIDER: S-EPMC8416760 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature