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Optimising Retraining Frequency for a Paediatric Emergency Department Admission Prediction Model: Development and

Ethan Williams1,2, Toshi Sinha1,2, Mark Lyttle1

  • 1Perth Children's Hospital Emergency Department, Nedlands, Australia.

Emergency Medicine Australasia : EMA
|May 6, 2026
PubMed
Summary

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This summary is machine-generated.

Monthly retraining of machine learning models for paediatric emergency department (ED) admissions prediction is optimal. This approach mitigates concept drift and ensures accurate daily bed-demand forecasting.

Area of Science:

  • Machine Learning in Healthcare
  • Clinical Informatics
  • Predictive Analytics

Background:

  • Paediatric emergency departments (EDs) face challenges in predicting inpatient admissions.
  • Accurate prediction is crucial for resource allocation and patient flow management.
  • Temporal performance drift in predictive models necessitates regular retraining.

Purpose of the Study:

  • To analyze temporal performance drift in an ensemble machine learning model for predicting paediatric ED admissions.
  • To determine the optimal retraining frequency for sustained model accuracy and calibration.
  • To evaluate the impact of retraining cadences on computational burden.

Main Methods:

  • Utilized 409,307 ED presentations from a single tertiary paediatric hospital.
Keywords:
admission predictionartificial intelligenceconcept driftmachine learningpaediatric emergency medicine

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  • Developed an ensemble stacking model incorporating structured triage data and BioClinicalBERT embeddings.
  • Conducted a 5-year rolling-window simulation testing retraining frequencies from weekly to triennial.

    • Main Results:

    • Weekly retraining yielded a mean AUROC of 0.843 and AMDBE of 2.57.
    • Monthly retraining demonstrated non-inferior performance with significantly reduced computational cost (25% of weekly).
    • Longer retraining intervals led to progressive calibration degradation and increased concept drift.

    Conclusions:

    • Monthly or more frequent retraining is essential for paediatric ED admission prediction models.
    • Regular retraining effectively mitigates concept drift and maintains model calibration.
    • This study supports the clinical implementation of regularly retrained predictive models for bed-demand forecasting.