Related Experiment Video
Updated: Jun 27, 2025
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Development of a machine learning model and nomogram to predict seizures in children with COVID-19: a two-center
Yu-Qi Liu1, Wei-Hua Yuan2, Yue Tao1
1Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China.
Insights
Machine learning models can predict seizures in children with coronavirus disease 2019 (COVID-19). The random forest model, incorporating neutrophil percentage, cough, and fever duration, showed the best predictive performance for pediatric seizures.
Area of Science:
- Pediatric Neurology
- Infectious Diseases
- Machine Learning in Medicine
Background:
- Coronavirus disease 2019 (COVID-19) can affect children, sometimes leading to neurological complications like seizures.
- Predicting seizures in pediatric COVID-19 cases is crucial for timely intervention and management.
Purpose of the Study:
- To identify risk factors associated with seizures in children diagnosed with COVID-19.
- To develop and validate a machine learning model and nomogram for predicting seizures in this population.
Main Methods:
- Utilized machine learning algorithms including extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) on data from 519 children with COVID-19.
- Model performance was evaluated using area under the receiver operating characteristic curve (AUC).
- Feature importance was assessed using SHapley Additive exPlanations (SHAP) values; a nomogram and clinical impact curve were used for validation.
Main Results:
- Out of 519 children, 217 experienced seizures.
- The random forest (RF) model demonstrated superior performance with the highest AUC.
- Key predictors identified by SHAP values included neutrophil percentage, cough, and fever duration.
Conclusions:
- The developed RF model exhibits excellent accuracy in predicting seizures in children with COVID-19.
- The novel nomogram serves as a valuable tool for clinical decision-making, aiding in the prevention and treatment of seizures.
Objective:
This study aimed to use machine learning to evaluate the risk factors of seizures and develop a model and nomogram to predict seizures in children with coronavirus disease 2019 (COVID-19).
Material And Methods:
A total of 519 children with COVID-19 were assessed to develop predictive models using machine learning algorithms, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR). The performance of the models was assessed using area under the receiver operating characteristic curve (AUC) values. Importance matrix plot and SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and to show the visualization results. The nomogram and clinical impact curve were used to validate the final model.
Results:
Two hundred and seventeen children with COVID-19 had seizures. According to the AUC, the RF model performed the best. Based on the SHAP values, the top three most important variables in the RF model were neutrophil percentage, cough and fever duration. The nomogram and clinical impact curve also verified that the RF model possessed significant predictive value.
Conclusions:
Our research indicates that the RF model demonstrates excellent performance in predicting seizures, and our novel nomogram can facilitate clinical decision-making and potentially offer benefit for clinicians to prevent and treat seizures in children with COVID-19.
