Key result
Multiple linear regression and Random Forest models effectively predicted length of stay in patients with lower limb fractures, achieving an adjusted R-square of 0.805 and an accuracy of 75.7%, respectively.
Why the study?
Predicting hospital length of stay in orthopedics is increasingly complex yet critical for resource planning, waiting times, and department management.
Can machine learning and multiple linear regression accurately predict the length of stay for patients hospitalized with lower limb fractures?
Observational (n=706)
No
Can machine learning and multiple linear regression accurately predict the length of stay for patients hospitalized with lower limb fractures?
Machine learning and multiple linear regression models can effectively predict the length of hospital stay for patients with lower limb fractures, identifying preoperative LOS and age as significant predictors.
Answers are grounded in this paper and cite their sources.
Could support bed management in orthopedic wards; leaves open external validation before clinical adoption.
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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Scala et al. (2022) conducted an observational in Lower limb fractures (n=706). Machine learning and multiple linear regression models was evaluated on Prediction of Length of Stay (LOS) (Adjusted R-square 0.805, p=<0.001). Multiple linear regression and Random Forest models effectively predicted length of stay in patients with lower limb fractures, achieving an adjusted R-square of 0.805 and an accuracy of 75.7%, respectively.
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