Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning.

ABSTRACT: Sepsis-associated encephalopathy (SAE) is a major complication of sepsis and is associated with high mortality and poor long-term prognosis. The purpose of this study is to develop interpretable machine learning models to predict the occurrence of SAE after ICU admission and implement the individual prediction and analysis. Patients with sepsis admitted to ICU were included. SAE was diagnosed as glasgow coma score (GCS) less than 15. Statistical analysis at baseline was performed between SAE and non-SAE. Six machine learning classifiers were employed to predict the occurrence of SAE, and the adjustment of model super parameters was performed by using Bayesian optimization method. Finally, the optimal algorithm was selected according to the prediction efficiency. In addition, professional p

SUBMITTER: Lu X 

PROVIDER: S-EPMC9805434 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature