Development and validation of a machine learning model to predict prognostic outcomes in infantile epileptic spasms syndrome.
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R · 29 lines · 2 KB · no license · 1 match
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Overview
- Department of Neurology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- State Key Laboratory of Material Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
- Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Institute of Maternal and Child Health, Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
Abstract
Objective: To develop and validate a machine learning (ML) model for predicting seizure outcomes in infants with infantile epileptic spasms syndrome (IESS).
Methods: This retrospective study enrolled pediatric patients diagnosed with infantile epileptic spasms syndrome (IESS) from Wuhan Children's Hospital. The cohort was randomly split into training and validation sets at a 7:3 ratio. Independent prognostic factors were identified using Cox regression analysis. Six machine learning algorithms were then applied to develop predictive models. Model performance was evaluated in terms of discrimination (e.g., AUROC), calibration (calibration curves), and clinical utility (decision curve analysis, DCA). The optimal model (XGBoost) was interpreted via decision tree visualization and SHAP analysis.
Results: Poor seizure outcome was observed in 56% of the cohort. MRI findings of tuberous sclerosis complex or malformations of cortical development were independent risk factors. Among the models, XGBoost demonstrated the best overall performance, achieving an AUROC of 0.921 in the validation set, along with robust calibration and clinical utility.
Conclusion: The developed ML model reliably and interpretably predicts poor seizure outcomes in IESS patients using routine clinical data, potentially aiding in clinical decision-making and follow-up planning.
Reproduced under the paper's license (CC BY), from the paper cited above.
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mdbrown/rmda
84a11aff0e21793a834e1127968da644739c6bed, 16 October 2018Availability: 1 check, the latest on 29 September 2026: the link answers
- 29 September 2026: the link answers
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data.R — R, 33 lines, shown from its source - R/
decision_curve.R — R, 293 lines, shown from its source - R/
plot_functions_main.R — R, 403 lines, shown from its source - R/
plot_functions_sub.R — R, 285 lines, shown from its source - R/
rmda.R — R, 29 lines, 1 match, shown from its source - R/
subroutines.R — R, 158 lines, shown from its source - R/
summary.decision_curve.R — R, 133 lines, shown from its source - inst/
notes/ — R, 377 lines, shown from its sourcetutorial.Rmd - README.md — Text, 50 lines, shown from its source
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Data availability statement
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Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 1 funder, 59 references.
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This paper
Zuo, C., Xue, B., Mu, X., Cao, Z., Zhou, H., Liu, Z., & Sun, D. (2026). Development and validation of a machine learning model to predict prognostic outcomes in infantile epileptic spasms syndrome. Frontiers in pediatrics, 14, 1777561. https://
BibTeX
@article{zuo2026developm
author = {Zuo, Caoxue and Xue, Boen and Mu, Xiaofeng and Cao, Zhongqiang and Zhou, Huamin and Liu, Zhisheng and Sun, Dan},
title = {{Development and validation of a machine learning model to predict prognostic outcomes in infantile epileptic spasms syndrome}},
journal = {Frontiers in pediatrics},
year = {2026},
month = apr,
volume = {14},
pages = {1777561},
publisher = {Frontiers Media SA},
issn = {2296-2360},
doi = {10.3389/
url = {https://
pmid = {42023283},
pmcid = {PMC13096072}
}
RIS
TY - JOUR
AU - Zuo, Caoxue
AU - Xue, Boen
AU - Mu, Xiaofeng
AU - Cao, Zhongqiang
AU - Zhou, Huamin
AU - Liu, Zhisheng
AU - Sun, Dan
TI - Development and validation of a machine learning model to predict prognostic outcomes in infantile epileptic spasms syndrome
T2 - Frontiers in pediatrics
J2 - Front Pediatr
PY - 2026
DA - 2026/
VL - 14
SP - 1777561
SN - 2296-2360
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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