Functional gastrointestinal disorders predictors in neonates and toddlers: A machine learning approach to risk assessment.

ABSTRACT:

Background

Functional Gastrointestinal Disorders (FGIDs) can pose a great burden on affected children, their families, and the healthcare system. Due to the lack of knowledge about the precise pathophysiology of FGIDs, a proper identification of children at risk to develop FGIDs has never been attempted. The research aims to identify early-life risk factors for FGIDs such as infantile colic, regurgitation, and functional constipation, within the first year of life.

Methods

This prospective observational cohort study enrolled both term and preterm infants from a tertiary care university hospital between January 1, 2020, and December 31, 2022. The study employed both traditional statistical methods and artificial intelligence (AI) techniques, specifically a random forest class

SUBMITTER: Indrio F 

PROVIDER: S-EPMC11743318 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature