Hospitalization duration after ophthalmic surgery varies widely, affecting costs, resource use, and outcomes. Length of stay (LOS) is key for hospital efficiency and patient management. Prolonged stays raise expenses and strain capacity, while early discharge risks complications. Accurate LOS prediction helps optimize care and reduce costs. This study developed a machine learning model to estimate LOS for ophthalmic surgery patients at A.O. "A. Cardarelli" in Naples, Italy. Using neural networks and decision tree-based models, we evaluated their predictive accuracy, highlighting AI’s potential to improve planning and care in ophthalmology.
Predicting Length of Stay in Ophthalmology Patients: A Neural Network Approach
Santalucia I.;Toscano A.;
2025-01-01
Abstract
Hospitalization duration after ophthalmic surgery varies widely, affecting costs, resource use, and outcomes. Length of stay (LOS) is key for hospital efficiency and patient management. Prolonged stays raise expenses and strain capacity, while early discharge risks complications. Accurate LOS prediction helps optimize care and reduce costs. This study developed a machine learning model to estimate LOS for ophthalmic surgery patients at A.O. "A. Cardarelli" in Naples, Italy. Using neural networks and decision tree-based models, we evaluated their predictive accuracy, highlighting AI’s potential to improve planning and care in ophthalmology.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
