Machine learning prediction of oxygen therapy in pediatric Mycoplasma pneumoniae pneumonia
Machine learning models using routine admission data can accurately predict oxygen therapy requirements in pediatric Mycoplasma pneumoniae pneumonia. The integration of interpretable artificial intelligence approaches may enable earlier risk stratification and improve clinical decision-making in pediatric respiratory infections.
Authors
- Maria Rosaria La Bianca
- Claudio Coppola (ORCID: https://orcid.org/0000-0002-3835-9268)
- Milena La Spina (ORCID: https://orcid.org/0000-0002-0176-7380)
- Judith Jeyafreeda Andrew
- Salvatore Leonardi
Institutions
- Inserm (FR)
- Université Paris Cité (FR)
- Art Institute of Portland (US)
- University of Catania (IT)
- Ospedale Sant Antonio (IT)
- Hospital de Santo António (PT)
- Institut des Maladies Génétiques Imagine (FR)
- Universidad San Marcos (CR)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2026-02-19
- DOI
- https://doi.org/10.3389/fdgth.2026.1755878
- Primary Topic
- Pneumonia and Respiratory Infections
- Type
- article
- Field-Weighted Citation Impact
- 0.00