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Systematic Review of Studies Investigating Infant Feeding Difficulties: Focus on Machine Learning Applications.

Eun Sun Ji1, Kyoung Ju Lee2, Yoon Chung Chung3

  • 1Professor, Department of Nursing, Konkuk University Glocal Campus, Chungju, South Korea.

Journal of Pediatric Health Care : Official Publication of National Association of Pediatric Nurse Associates & Practitioners
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PubMed
Summary

Machine learning shows promise in analyzing infant feeding behaviors to identify difficulties. Further research is needed to standardize methods and enhance clinical application of these predictive models.

Keywords:
Preterm infantsdeep learningdysphagiaoral motor skillspredictive models

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Area of Science:

  • Biomedical Engineering
  • Pediatrics
  • Artificial Intelligence

Background:

  • Infant feeding difficulties present significant clinical challenges.
  • Machine learning (ML) offers potential for objective assessment of infant feeding.
  • Systematic review of ML applications in this field is crucial.

Purpose of the Study:

  • To systematically review studies utilizing ML for infant feeding difficulties.
  • To evaluate the clinical applicability of ML approaches.
  • To identify limitations of current ML methods in infant feeding assessment.

Main Methods:

  • PRISMA guidelines were followed for a comprehensive literature search.
  • Studies employing ML for infant feeding difficulties were analyzed.
  • Included studies focused on sensor-based physiological signal measurement.

Main Results:

  • Ten studies met inclusion criteria after screening 1,104.
  • ML was used to classify/predict feeding difficulties via physiological signals (sucking, swallowing, breathing).
  • Common ML algorithms (SVMs, KNNs, DTs) demonstrated high classification accuracy.

Conclusions:

  • Sensor data was used in all studies, but direct ML application was limited (4 studies).
  • Standardization of measurements and algorithm validation are essential for clinical utility.
  • Future research should focus on expanding direct ML applications and improving reliability.