A Literature Review on Length of Stay Prediction for Stroke Patients using Machine Learning and Statistical Approaches

• URL: http://arxiv.org/abs/2201.00005v1
• Date: Thu, 30 Dec 2021 03:48:41 GMT
• Title: A Literature Review on Length of Stay Prediction for Stroke Patients using Machine Learning and Statistical Approaches
• Authors: Ola Alkhatib and Ayman Alahmar
• Abstract summary: Hospital length of stay (LOS) is one of the most essential healthcare metrics that reflects the hospital quality of service and helps improve hospital scheduling and management. In this study, we reviewed papers on LOS prediction using machine learning and statistical approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Hospital length of stay (LOS) is one of the most essential healthcare metrics that reflects the hospital quality of service and helps improve hospital scheduling and management. LOS prediction helps in cost management because patients who remain in hospitals usually do so in hospital units where resources are severely limited. In this study, we reviewed papers on LOS prediction using machine learning and statistical approaches. Our literature review considers research studies that focus on LOS prediction for stroke patients. Some of the surveyed studies revealed that authors reached contradicting conclusions. For example, the age of the patient was considered an important predictor of LOS for stroke patients in some studies, while other studies concluded that age was not a significant factor. Therefore, additional research is required in this domain to further understand the predictors of LOS for stroke patients.

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