Functional gastrointestinal disorders predictors in neonates and toddlers: A machine learning approach to risk assessment
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.
Authors
- Elio Masciari (ORCID: https://orcid.org/0000-0002-1778-5321)
- Assunta Grillo
- Flavia Marchese
- Matteo Rinaldi (ORCID: https://orcid.org/0000-0002-3568-5973)
- Enea Vincenzo Napolitano (ORCID: https://orcid.org/0000-0002-6384-9891)
- Antonio Di Mauro (ORCID: https://orcid.org/0000-0001-7052-9784)
- Arianna Aceti (ORCID: https://orcid.org/0000-0001-9274-985X)
- Luigi Corvaglia (ORCID: https://orcid.org/0000-0002-8119-7743)
- Isadora Beghetti (ORCID: https://orcid.org/0000-0003-4819-1830)
- Gianfranco Maffei
- Flavia Indrio (ORCID: https://orcid.org/0000-0001-9789-7878)
- Ilaria Gangai
- Roberta De Benedetto
Institutions
- University of Foggia (IT)
- University of Salento (IT)
- Ospedali Riuniti di Foggia (IT)
- Federico II University Hospital (IT)
- Primary Health Care (QA)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- University of Bologna (IT)
Publication Details
- Journal
- Heliyon
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e41516
- Primary Topic
- Infant Health and Development
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission