High Length of Stay values (LOS) have a significant impact on the economy of the national health system. The LOS is influenced by numerous factors such as poor resources management and surgical interventions. In this work Machine Learning models have been built to predict LOS. We collect information about 138 patients from the hospital information system of the Complex Operating Units of Ophthalmology of the A.O.R.N. “Antonio Cardarelli” of Naples (Italy). The analysis was conducted with the implementation of the following Machine Learning algorithms. All models obtained an accuracy greater than 80%. The algorithms that obtained a higher accuracy were RF and GBT, with a value of 85.71%.
Use of Classification Algorithms to Investigate Inpatient Stay for Retinal Diseases / Montella, E.; Marino, M. R.; Giglio, C.; Majolo, M.; Longo, G.; Triassi, M.; Scala, A.. - 13637 LNBI:(2023), pp. 496-503. (Intervento presentato al convegno International Symposium on Biomedical and Computational Biology) [10.1007/978-3-031-25191-7_47].
Use of Classification Algorithms to Investigate Inpatient Stay for Retinal Diseases
Montella E.;Majolo M.;Triassi M.;Scala A.Ultimo
2023
Abstract
High Length of Stay values (LOS) have a significant impact on the economy of the national health system. The LOS is influenced by numerous factors such as poor resources management and surgical interventions. In this work Machine Learning models have been built to predict LOS. We collect information about 138 patients from the hospital information system of the Complex Operating Units of Ophthalmology of the A.O.R.N. “Antonio Cardarelli” of Naples (Italy). The analysis was conducted with the implementation of the following Machine Learning algorithms. All models obtained an accuracy greater than 80%. The algorithms that obtained a higher accuracy were RF and GBT, with a value of 85.71%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
