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Machine learning models to predict electroencephalographic seizures in critically ill children
Jian Hu1, France W Fung2, Marin Jacobwitz3
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, United States.
Insights
Machine learning identified key variables for electroencephalographic seizure (ES) prediction in critically ill children. While these models improved variable selection, they did not significantly outperform previous methods, suggesting a need for more data.
Area of Science:
- Pediatric Neurology
- Computational Neuroscience
- Critical Care Medicine
Background:
- Electroencephalographic seizures (ES) are a significant concern in critically ill children.
- Accurate prediction of ES is crucial for timely intervention and improved patient outcomes.
- Existing models for ES prediction may lack optimal performance and parsimony.
Purpose of the Study:
- To evaluate machine learning techniques for enhancing electroencephalographic seizure (ES) prediction in critically ill children.
- To identify key variables for a parsimonious ES prediction model with optimized performance.
- To compare the performance of machine learning-based variable selection against traditional methods.
Main Methods:
- Analysis of prospective observational data from 719 critically ill children undergoing continuous EEG monitoring (CEEG).
- Implementation and comparison of random forest, LASSO, and DeepLIFT machine learning methods for ES prediction.
- Development of a ranking algorithm based on variable importance derived from machine learning models.
Main Results:
- The top five predictors for ES were: epileptiform discharges, prior clinical seizures, sex, age (dichotomized at 1 year), and epileptic encephalopathy.
- Machine learning-derived variable rankings led to more informative models with superior prediction accuracy, AUROC, and F1 scores compared to stepwise logistic regression.
- Inclusion of additional variables did not consistently improve, and sometimes degraded, model performance.
Conclusions:
- A machine learning-based ranking algorithm effectively identified key variables for a parsimonious and high-performing ES prediction model.
- State-of-the-art machine learning models did not substantially enhance prediction performance over prior logistic regression models.
- Future improvements in ES prediction may necessitate the collection of additional samples and more informative variables.
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 the machine learning methods.
Results:
Based on our ranking algorithm, the top five variables for ES prediction were: (1) epileptiform discharges in the initial 30 minutes, (2) clinical seizures prior to CEEG initiation, (3) sex, (4) age dichotomized at 1 year, and (5) epileptic encephalopathy. Compared to the stepwise selection-based approach in logistic regression, the top variables selected by our ranking algorithm were more informative as models utilizing the top variables achieved better prediction performance evaluated by prediction accuracy, AUROC and F1 score. Adding additional variables did not improve and sometimes worsened model performance.
Conclusion:
The ranking algorithm was helpful in deriving a parsimonious model for ES prediction with optimal performance. However, application of state-of-the-art machine learning models did not substantially improve model performance compared to prior logistic regression models. Thus, to further improve the ES prediction, we may need to collect more samples and variables that provide additional information.
