Artificial intelligence and precision medicine: a pilot study predicting optimal ceftaroline dosage for pediatric patients
Advanced ML models can optimize ceftaroline dosing in pediatric patients and outperform traditional dosing strategies. Combining predictive accuracy with interpretability (via LIME) supports clinical trust and may enhance precision antibiotic therapy while reducing the risks of antimicrobial resistance and toxicity.
Authors
- Valentina De Nicolò (ORCID: https://orcid.org/0000-0002-2599-4430)
- Davide La Torre (ORCID: https://orcid.org/0000-0003-2776-0037)
- Francesco Scaglione (ORCID: https://orcid.org/0000-0002-8554-2401)
- Romano Danesi
- Alessandra Romandini (ORCID: https://orcid.org/0000-0002-9150-425X)
- Gianluca Gazzaniga (ORCID: https://orcid.org/0000-0003-0648-3309)
- Maria Frasca (ORCID: https://orcid.org/0000-0003-3164-1858)
- Gianluca Gaetano Vago
- Michele Senatore
- Agnese Graziosi
- Arianna Pani
- Chiara Moretti
- Stefano Martinelli
- Alice Proto
- Giulia Angela Carla Pattarino
Institutions
- SKEMA Business School (FR)
- University of Milan (IT)
- Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda (IT)
- Sapienza University of Rome (IT)
- University of Bologna (IT)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-01-16
- DOI
- https://doi.org/10.3389/frai.2025.1702087
- Primary Topic
- Pharmaceutical studies and practices
- Type
- article
- Field-Weighted Citation Impact
- 0.00