Keywords
Predictive modeling
Length of stay
Hospitals
Healthcare
Resource allocation
Discharge planning
Clinical data
Patient attributes
How to Cite
Abstract
The accurate prediction of a patient's length of stay (LOS) in a hospital is crucial for efficient resource allocation, bed management, and discharge planning. Machine learning (ML) models offer a promising approach to predict LOS based on various patient attributes and clinical data. This paper presents a comprehensive review of recent advances in using ML for predictive modeling of patient LOS in hospitals. We discuss the challenges, methodologies, and outcomes of these models, highlighting their potential benefits for healthcare systems. Our study demonstrates the effectiveness of ML in predicting patient LOS and its impact on improving hospital operations and patient care.
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