Reassessing acquired neonatal intestinal diseases using unsupervised machine learning

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.PMID:38413766 | DOI:10.1038/s41390-024-03074-x
Source: Pediatric Research - Category: Pediatrics Authors: Source Type: research