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Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.
Gipson, Daniel R; Chang, Alan L; Lure, Allison C; Mehta, Sonia A; Gowen, Taylor; Shumans, Erin; Stevenson, David; de la Cruz, Diomel; Aghaeepour, Nima; Neu, Josef.
Afiliación
  • Gipson DR; University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA. daniel.gipson@ufl.edu.
  • Chang AL; Stanford University School of Medicine, Department of Anesthesiology, Pain, and Perioperative Medicine, Department of Pediatrics, and Department of Biomedical Data Science, Stanford, CA, USA.
  • Lure AC; Nationwide Children's Hospital, The Ohio State University College of Medicine, Department of Pediatrics, Division of Neonatology, Columbus, OH, USA.
  • Mehta SA; University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA.
  • Gowen T; University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA.
  • Shumans E; University of California, Irvine Medical Center, Department of Pediatrics, Division of Neonatology, Irvine, CA, USA.
  • Stevenson D; University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA.
  • de la Cruz D; University of Florida College of Medicine, Department of Anesthesiology, Gainesville, FL, USA.
  • Aghaeepour N; University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA.
  • Neu J; Stanford University School of Medicine, Department of Pediatrics, Division of Neonatology, Stanford, CA, USA.
Pediatr Res ; 96(1): 165-171, 2024 Jul.
Article en En | MEDLINE | ID: mdl-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.

METHODS:

Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis.

RESULTS:

Five clusters of intestinal injury were identified Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster.

CONCLUSION:

Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases. IMPACT Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Aprendizaje Automático no Supervisado / Enfermedades Intestinales Límite: Female / Humans / Male / Newborn Idioma: En Revista: Pediatr Res / Pediatr. res / Pediatric research Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Aprendizaje Automático no Supervisado / Enfermedades Intestinales Límite: Female / Humans / Male / Newborn Idioma: En Revista: Pediatr Res / Pediatr. res / Pediatric research Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos