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Functional gastrointestinal disorders predictors in neonates and toddlers: A machine learning approach to risk
Flavia Indrio1, Elio Masciari2, Flavia Marchese3
1Department of Experimental Medicine School of Medicine University of Salento, Lecce, Italy.
Insights
Researchers identified early-life risk factors for functional gastrointestinal disorders (FGIDs) in infants. Birth weight, cord blood pH, and maternal age are key predictors, enabling early identification and tailored prevention strategies for FGIDs.
Area of Science:
- Pediatrics
- Gastroenterology
- Artificial Intelligence in Medicine
Background:
- Functional Gastrointestinal Disorders (FGIDs) present a significant burden on children, families, and healthcare systems.
- The precise pathophysiology of FGIDs remains largely unknown, hindering early identification of at-risk infants.
- This research focuses on identifying early-life risk factors for FGIDs within the first year of life.
Purpose of the Study:
- To identify early-life risk factors for functional gastrointestinal disorders (FGIDs) in infants.
- To develop an AI-based predictive model for FGID risk assessment.
- To create a practical, web-based tool for clinicians to identify infants at risk.
Main Methods:
- Prospective observational cohort study of term and preterm infants (n=6060).
- Utilized traditional statistical methods and a random forest classification model (AI).
- Identified key risk factors associated with FGID development in the first year of life.
Main Results:
- Artificial intelligence identified birth weight, cord blood pH, and maternal age as significant predictors of FGIDs.
- Discrepancies were noted between risk factors identified by conventional statistics and AI.
- The AI model highlighted specific variables crucial for early FGID risk prediction.
Conclusions:
- Machine learning, for the first time, identified birth weight, cord blood pH, and maternal age as critical variables for predicting FGIDs in infants.
- The developed AI-based risk assessment tool can aid clinicians in identifying infants who would benefit from preventive interventions.
- This approach facilitates a tailored preventive strategy for infants at risk of developing FGIDs.
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 classification model, to identify key risk factors associated with the development of FGIDs. Based on these findings, an AI-based predictive model will be developed, along with a user-friendly, web-based interface designed for practical risk assessment.
Results:
6060 infants were enrolled. 8.1 % were born preterm. According to random forest classification model by AI, birth weight (BW), cord blood pH, and maternal age were the most relevant variables linked to development of FGIDs in the first year of life. Some discrepancies between potential risk factors identified through conventional statistics and AI were detected.
Conclusion:
For the first time machine learning allowed to identify BW, cord blood pH and maternal age as important variable for risk prediction of FGIDs in the first year of life. This practical risk assessment tool would help clinicians to identify infants at risk of FGIDs who would benefit from a tailored preventive approach.
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