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Related Experiment Videos

Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight

Joohee Lim1, Sook Hyun Park2, Teahyen Cha1

  • 1Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.

Diagnostics (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

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This summary is machine-generated.

A machine-learning model accurately predicted postnatal growth failure (PGF) in preterm infants, outperforming neonatologists. This AI tool can aid in early risk stratification and targeted nutritional support for very low birth weight (VLBW) infants.

Area of Science:

  • Neonatalogy
  • Artificial Intelligence in Medicine
  • Biostatistics

Background:

  • Postnatal growth failure (PGF) in preterm infants is linked to poor neurodevelopmental outcomes.
  • Predicting PGF is challenging due to multiple influencing clinical factors.
  • Machine learning (ML) offers potential for predicting complex neonatal outcomes.

Purpose of the Study:

  • To compare the predictive performance of neonatologists with an ML model for PGF detection.
  • To evaluate the efficacy of an extreme gradient boosting (XGBoost) model in identifying PGF risk.

Main Methods:

  • Defined PGF as a weight z-score decrease >1.28 from birth to discharge.
  • Trained an XGBoost ML model on data from 7954 very low birth weight (VLBW) infants.
  • Assessed 100 clinical cases (50 with PGF) using nine neonatologists and the XGBoost model, evaluating seven performance metrics.
Keywords:
artificial intelligenceclinical decision supportmachine learningpostnatal growth failureprediction modelvery low birth weight infants

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Main Results:

  • The XGBoost model achieved a higher prediction score (79/100) than neonatologists (median 52/100).
  • XGBoost demonstrated superior performance with an AUROC of 0.79 and F1 score of 0.80.
  • The ML model showed a significantly lower error rate (0.21) compared to neonatologists (0.49).

Conclusions:

  • ML models exhibit superior or comparable predictive performance to neonatologists for PGF detection.
  • ML-based prediction tools can enhance early risk stratification for VLBW infants.
  • These models support timely and targeted nutritional interventions in preterm neonates.