The estimation of the Length of Stay (LOS) is a critical factor in clinical and managerial decision-making, helping healthcare professionals optimize hospital efficiency. For patients with orthopedic trauma, particularly those with lower limb fractures, LOS prediction becomes essential for resource planning and improving patient care. This study aims to analyze and predict LOS for patients with lower limb fractures admitted to the A.O.R.N. “Antonio Cardarelli” hospital in Naples. To achieve this, five neural network-based classifiers were implemented, and their performances were compared with those obtained in previous studies conducted by our research group, which employed well-established Artificial Intelligence (AI) models.
The Role of Machine Learning in LOS Reduction for Patients Affected by Lower Limb Fracture
Santalucia I.;
2025-01-01
Abstract
The estimation of the Length of Stay (LOS) is a critical factor in clinical and managerial decision-making, helping healthcare professionals optimize hospital efficiency. For patients with orthopedic trauma, particularly those with lower limb fractures, LOS prediction becomes essential for resource planning and improving patient care. This study aims to analyze and predict LOS for patients with lower limb fractures admitted to the A.O.R.N. “Antonio Cardarelli” hospital in Naples. To achieve this, five neural network-based classifiers were implemented, and their performances were compared with those obtained in previous studies conducted by our research group, which employed well-established Artificial Intelligence (AI) models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
