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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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An interpretable machine learning model for predicting emergence agitation in children: a multicenter development and

Qingyu Zhao1, Yi Zhang2, Rugang An3

  • 1Department of Anesthesiology, The First Affiliated Hospital of Army Medical University (Southwest Hospital), Chongqing, 400038, China.

BMC Anesthesiology
|May 14, 2026
PubMed
Summary

This study developed a machine learning model to predict emergence agitation (EA) in pediatric patients, identifying key risk factors like parental education and ALT levels. The model aids in early risk stratification for improved perioperative safety.

Keywords:
Emergence agitationMachine learningPediatricRisk predictionSHAP

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Published on: December 2, 2015

Area of Science:

  • Pediatric Anesthesiology
  • Machine Learning in Healthcare
  • Perioperative Medicine

Background:

  • Emergence agitation (EA) is a frequent complication in pediatric surgery, impacting patient safety.
  • Early identification of EA risk factors is crucial for effective perioperative management.

Purpose of the Study:

  • To identify risk factors for pediatric EA.
  • To develop and validate an interpretable machine learning model for predicting EA risk.

Main Methods:

  • A multicenter retrospective study of 445 pediatric patients.
  • Feature selection using LASSO regression and evaluation of six machine learning algorithms (LR, SVM, MLP, RF, XGB, LGBM).
  • Model interpretability assessed using SHapley Additive Explanations (SHAP).

Main Results:

  • Five key predictors identified: parental education, ALT level, analgesic pump use, antagonist administration, and suctioning maneuvers.
  • The Multilayer Perceptron (MLP) model showed optimal performance in external validation (AUC 0.705).
  • SHAP analysis highlighted parental education, ALT, analgesic pump use, antagonist, and suctioning as significant risk factors.

Conclusions:

  • An interpretable machine learning model was developed and externally validated for predicting pediatric EA risk.
  • The model incorporates readily available clinical variables for risk stratification.
  • Further prospective studies are needed to confirm clinical utility and generalizability.