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Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities.

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Machine learning (ML) offers significant promise for improving paediatric care by predicting disease and treatment outcomes. Proper data preparation and validation are crucial for ML

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Area of Science:

  • Pediatric Medicine
  • Artificial Intelligence
  • Machine Learning

Background:

  • Machine learning (ML) applications are increasingly prevalent in daily life but underutilized in healthcare.
  • Pediatricians manage complex, heterogeneous long-term conditions, presenting opportunities for ML-driven insights.
  • Understanding ML foundations and applications is currently limited within the medical community.

Purpose of the Study:

  • To review the fundamental principles and algorithms of machine learning.
  • To highlight the importance of proper data preparation and external validation in ML applications.
  • To explore the current and potential utility of ML in pediatrics using clinical examples.

Main Methods:

  • Review of machine learning foundations and algorithms.
  • Discussion of ML applications using case studies in preterm infant nutrition and pediatric inflammatory bowel disease.
  • Examination of challenges and ethical considerations for AI in pediatrics.

Main Results:

  • Machine learning holds significant promise for improving pediatric care through predictive models and patient subgroup identification.
  • Clinical examples demonstrate the potential utility of ML in areas like preterm infant nutrition and pediatric inflammatory bowel disease.
  • Effective ML implementation requires careful data preparation and external validation.

Conclusions:

  • Machine learning has the potential to significantly advance pediatric medicine, particularly for complex conditions.
  • Further research and careful implementation are needed to overcome challenges and ethical considerations.
  • This review aims to bridge the understanding gap regarding ML in pediatrics and neonatal medicine.