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Updated: Jun 3, 2025

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic

Jehier Afifi¹, Tahani Ahmad², Alessandro Guida²

¹Division of Neonatal Perinatal Medicine, Department of Pediatrics, Dalhousie University, Halifax, NS B3K 6R8, Canada.

Children (Basel, Switzerland)

|January 8, 2025

Summary

This summary is machine-generated.

Machine learning, specifically random forest, accurately predicts neurodevelopmental impairment in very preterm infants. This aids early intervention for high-risk infants.

Keywords:

machine learning neurodevelopment predictive modeling preterm infants

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Area of Science:

Neonatal Medicine
Developmental Pediatrics
Machine Learning in Healthcare

Background:

Preterm infants face risks of neurodevelopmental impairment (NDI).
Accurate prediction of NDI is crucial for timely intervention.
Traditional methods like logistic regression (LR) have limitations.

Purpose of the Study:

To compare machine learning (ML) models against logistic regression (LR) for predicting NDI in very preterm infants.
To develop and validate an ML model using clinical predictors for NDI at 36 months corrected age.

Main Methods:

Retrospective cohort study of very preterm infants (<31 weeks gestation).
Development and internal validation of LR, elastic net (EN), random forest (RF), and gradient boosting (XGB) models.
Comparison based on discrimination (AUC), calibration, and diagnostic properties.

Main Results:

Random forest (RF) demonstrated superior performance in predicting NDI, with a higher AUC (0.79) compared to XGB (0.74), EN (0.74), and LR (0.73).
All models showed good discrimination, but RF offered the best predictive accuracy.
Overlapping confidence intervals suggest further validation is beneficial.

Conclusions:

Random forest (RF) is superior to logistic regression (LR) for predicting neurodevelopmental impairment in very preterm infants.
Accurate NDI prediction facilitates early intervention and resource allocation.
ML models offer a promising approach for identifying at-risk preterm infants.