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Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.

Daniel R Gipson1, Alan L Chang2, Allison C Lure3,4

  • 1University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA. daniel.gipson@ufl.edu.

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|February 27, 2024
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Summary

Machine learning identified five distinct clusters of acquired neonatal intestinal injuries, moving beyond traditional classifications. This approach offers potential for improved diagnosis and targeted treatments for these critical infant conditions.

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Area of Science:

  • Neonatal Medicine
  • Computational Biology
  • Medical Informatics

Background:

  • Acquired neonatal intestinal diseases present with overlapping symptoms, often misclassified as necrotizing enterocolitis or spontaneous intestinal perforation.
  • This imprecise classification hinders accurate diagnosis and effective research into these conditions.

Purpose of the Study:

  • To re-evaluate the classification of neonatal intestinal diseases.
  • To apply unsupervised machine learning for a more precise categorization of acquired intestinal injuries in neonates.

Main Methods:

  • Retrospective chart review of neonates admitted to a specific NICU between 2013-2019 with intestinal injury or related imaging findings.
  • Exclusion of congenital conditions like gastroschisis and omphalocele.
  • Application of hierarchical, unsupervised clustering analysis to the collected data.

Main Results:

  • Identification of five distinct clusters of neonatal intestinal injury.
  • Cluster 1: Low Mortality
  • Cluster 2: Mature with Inflammation
  • Cluster 3: Immature with High Mortality
  • Cluster 4: Late Injury at Full Feeds
  • Cluster 5: Late Injury with High Rate of Intestinal Necrosis.

Conclusions:

  • Unsupervised machine learning effectively clusters acquired neonatal intestinal injuries.
  • The identified clusters possess unique characteristics, suggesting distinct disease entities.
  • Further multicenter studies are crucial for refining these classifications, identifying early biomarkers, and developing tailored therapies to improve outcomes.