Kidney disease is a very important disease in the hospital context, in fact, the kidneys play a fundamental role for the whole body by maintaining the homeostasis of water for removing excess fluid and toxic substances. Therefore, it is necessary to assess all possible comorbidities of the patient before kidney surgery and possible complications. It is therefore necessary to analyze the length of stay (LOS) for proper hospital planning to ensure efficient and effective services. In this work the Length of Stay (LOS) of 70 patients of the AORN “A. Cardarelli” Hospital in Naples in the years 2019–2021 were analyzed. Machine Learning techniques were applied, and the algorithms implemented were Decision Tree, Random Forest and Naïve Bayes.
Study of Variables Influencing LOS with Machine Learning in Patients with Kidney Disease / Trunfio, T. A.; Marino, M. R.; Giglio, C.; Majolo, M.; Raiola, E.; Festa, E.; Longo, G.; Triassi, M.; Scala, A.. - 13637 LNBI:(2023), pp. 577-583. (Intervento presentato al convegno 2022 International Symposium on Biomedical and Computational Biology) [10.1007/978-3-031-25191-7_57].
Study of Variables Influencing LOS with Machine Learning in Patients with Kidney Disease
Trunfio T. A.Primo
;Majolo M.;Raiola E.;Festa E.;Triassi M.;Scala A.Ultimo
2023
Abstract
Kidney disease is a very important disease in the hospital context, in fact, the kidneys play a fundamental role for the whole body by maintaining the homeostasis of water for removing excess fluid and toxic substances. Therefore, it is necessary to assess all possible comorbidities of the patient before kidney surgery and possible complications. It is therefore necessary to analyze the length of stay (LOS) for proper hospital planning to ensure efficient and effective services. In this work the Length of Stay (LOS) of 70 patients of the AORN “A. Cardarelli” Hospital in Naples in the years 2019–2021 were analyzed. Machine Learning techniques were applied, and the algorithms implemented were Decision Tree, Random Forest and Naïve Bayes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
