Data-driven Machine Learning Models for Risk Stratification and Prediction of Emergence Delirium in Pediatric Patients Underwent Tonsillectomy/Adenotonsillectomy
ML techniques are valuable tools for extracting insights and making accurate predictions from healthcare data. High-performing algorithm-based models can be implemented for clinical decision support systems, facilitating early identification and intervention for ED in pediatric patients. By investigating various variables, it is possible to assess risk and implement preventive measures effectively. Furthermore, unsupervised clustering reveals distinct patient subgroups, enabling personalized perioperative management strategies and enhancing overall patient care.
Authors
- Alessandro Simonini (ORCID: https://orcid.org/0000-0003-3788-7672)
- Marco Cascella (ORCID: https://orcid.org/0000-0002-5236-3132)
- Roberta Pallotto
- Maria Grazia Calevo (ORCID: https://orcid.org/0000-0002-6246-9299)
- Elena Giovanna Bignami (ORCID: https://orcid.org/0000-0001-9985-4126)
- Alessandro Vittori (ORCID: https://orcid.org/0000-0002-2377-3765)
- Ornella Piazza (ORCID: https://orcid.org/0000-0002-0316-5930)
- Jeevitha Murugan
Institutions
- University of Parma (IT)
- University of Salerno (IT)
- Istituto Giannina Gaslini (IT)
- St. Joseph's Institute of Technology (IN)
- Fondazione Ospedale Salesi (IT)
- Bambino Gesù Children's Hospital (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- St. Joseph’s College of Engineering (IN)
Publication Details
- Journal
- Annali Italiani di Chirurgia
- Published
- 2024-10-20
- DOI
- https://doi.org/10.62713/aic.3485
- Citations
- 3
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.27