论文标题
关于使用机器学习和统计方法的中风患者住宿预测时间的文献综述
A Literature Review on Length of Stay Prediction for Stroke Patients using Machine Learning and Statistical Approaches
论文作者
论文摘要
住院时间（LOS）是反映医院服务质量并有助于改善医院时间表和管理的最重要的医疗指标之一。 LOS预测有助于成本管理，因为保留在医院的患者通常会在资源受到严重限制的医院单位中这样做。在这项研究中，我们使用机器学习和统计方法回顾了有关LOS预测的论文。我们的文献综述考虑了研究中风患者LOS预测的研究。一些接受调查的研究表明，作者得出了矛盾的结论。例如，在某些研究中，患者的年龄被认为是中风患者的LOS的重要预测指标，而其他研究得出结论，年龄不是一个重要因素。因此，在该领域需要进行其他研究，以进一步了解中风患者的LOS的预测因子。
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.
