Publications by authors named "O Piazza"
Introduction: Effective pain management is crucial for patient care, impacting comfort, recovery, and overall well-being. Traditional subjective pain assessment methods can be challenging, particularly in specific patient populations. This research explores an alternative approach using computer vision (CV) to detect pain through facial expressions.
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- The study aims to develop machine learning models to identify factors linked to Emergence Delirium (ED) in pediatric patients undergoing tonsillectomy or adenotonsillectomy.
- After cleaning and analyzing a dataset of 423 cases, four predictive models (logistic regression, random forest, support vector machine, and gradient boosting) were tested, with the random forest model showing the best performance (AUC-ROC of 0.96).
- Key findings highlighted significant correlations between factors like age, weight, and surgery duration with ED risk, while K-means clustering identified distinct patient groups characterized by varying risk levels for ED.
We conducted a bibliometrics analysis utilizing the Web of Science database, selecting 1925 articles concerning artificial intelligence (AI) in nursing. The analysis utilized the network visualization tool VOSviewer to explore global collaborations, highlighting prominent roles played by the United States, China, and Japan, as well as institutional partnerships involving Columbia University and Harvard Medical School. Keyword analysis identified prevalent themes and co-citation analysis highlighted influential journals.
View Article and Find Full Text PDFBackground: The integration of telemedicine in pain management represents a significant advancement in healthcare delivery, offering opportunities to enhance patient access to specialized care, improve satisfaction, and streamline chronic pain management. Despite its growing adoption, there remains a lack of comprehensive data on its utilization in pain therapy, necessitating a deeper understanding of physicians' perspectives, experiences, and challenges.
Methods: A survey was conducted in Italy between January 2024 and May 2024.