Combining artificial intelligence and conventional statistics to predict bronchopulmonary dysplasia in very preterm infants using routinely collected clinical variables
ML algorithms might be useful to integrate conventional statistics in identifying novel risk factors, in addition to prematurity, for the development of BPD in very preterm infants. Specifically, the identification of AREDF status as an independent risk factor for BPD by both conventional statistics and ML highlights the opportunity to include detailed antenatal information in clinical predictive models for neonatal diseases.
Authors
- Sara Montagna (ORCID: https://orcid.org/0000-0001-5390-4319)
- Camilla Dionisi (ORCID: https://orcid.org/0000-0001-5558-2210)
- Arianna Aceti (ORCID: https://orcid.org/0000-0001-9274-985X)
- Luigi Corvaglia (ORCID: https://orcid.org/0000-0002-8119-7743)
- Silvia Martini (ORCID: https://orcid.org/0000-0003-2078-7114)
- Stefano Ferretti
- Giuliana Simonazzi (ORCID: https://orcid.org/0000-0002-7186-395X)
- Dalila Magno
- Andrea Gona
- M. Stelluti
Institutions
- University of Urbino (IT)
- Istituto delle Scienze Neurologiche di Bologna (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- University of Bologna (IT)
Publication Details
- Journal
- Pediatric Pulmonology
- Published
- 2024-08-16
- DOI
- https://doi.org/10.1002/ppul.27216
- Citations
- 6
- Primary Topic
- Neonatal Respiratory Health Research
- Type
- article
- Field-Weighted Citation Impact
- 2.51