Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.

Journal: Pediatric research
PMID: 38413766

Abstract

BACKGROUND: Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning.

Authors

  • Daniel R Gipson
    University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA. daniel.gipson@ufl.edu.
  • Alan L Chang
    Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA.
  • Allison C Lure
    Nationwide Children's Hospital, The Ohio State University College of Medicine, Department of Pediatrics, Division of Neonatology, Columbus, OH, USA.
  • Sonia A Mehta
    University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA.
  • Taylor Gowen
    University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA.
  • Erin Shumans
    University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA.
  • David Stevenson
    Stanford University School of Medicine, Department of Pediatrics, Division of Neonatology, Stanford, CA, USA.
  • Diomel de la Cruz
    University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA.
  • Nima Aghaeepour
    Departments of Anesthesiology, Pain, and Peri-operative Medicine and Biomedical Data Sciences, Stanford University, Stanford, CA, USA.
  • Josef Neu
    Section of Neonatology, Department of Pediatrics, University of Florida, Gainesville, FL, USA.

Keywords

External Resources

Popular Topics
Recent Journals