Machine learning models to predict electroencephalographic seizures in critically ill children.

ABSTRACT:

Objective

To determine whether machine learning techniques would enhance our ability to incorporate key variables into a parsimonious model with optimized prediction performance for electroencephalographic seizure (ES) prediction in critically ill children.

Methods

We analyzed data from a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy who underwent clinically-indicated continuous EEG monitoring (CEEG). We implemented and compared three state-of-the-art machine learning methods for ES prediction: (1) random forest; (2) Least Absolute Shrinkage and Selection Operator (LASSO); and (3) Deep Learning Important FeaTures (DeepLIFT). We developed a ranking algorithm based on the relative importance of each variable derived from

SUBMITTER: Hu J 

PROVIDER: S-EPMC8044039 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature