ArticleScientific reports2022
Predictive analysis of lower limb fractures in the orthopedic complex operative unit using artificial intelligence: the case study of AOU Ruggi.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Epidemiology and Management of Ankle Fractures Prior to, During, and Following the COVID-19 Pandemic in an Italian Tertiary Hospital.Medicina (Kaunas, Lithuania) · 2025Observational
- Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.Scientific reports · 2024Article
- The classification algorithms to support the management of the patient with femur fracture.BMC medical research methodology · 2024Article
- Classification and regression model to manage the hospitalization for laparoscopic cholecystectomy.Scientific reports · 2023Article
- Predictive Analysis of Hospital Stay after Caesarean Section: A Single-Center Study.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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Abstract
The length of stay (LOS) in hospital is one of the main parameters for evaluating the management of a health facility, of its departments in relation to the different specializations. Healthcare costs are in fact closely linked to this parameter as well as the profit margin. In the orthopedic field, the provision of this parameter is increasingly complex and of fundamental importance in order to be able to evaluate the planning of resources, the waiting times for any scheduled interventions and the management of the department and related surgical interventions. The purpose of this work is to predict and evaluate the LOS value using machine learning methods and applying multiple linear regression, starting from clinical data of patients hospitalized with lower limb fractures. The data were collected at the "San Giovanni di Dio e Ruggi d'Aragona" hospital in Salerno (Italy).
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