Liu Yang, Liang Renzhao, Zhang Chengzhi
School of Information Management, Wuhan University, Wuhan, China.
Shenzhen Research Institute, Wuhan University, Shenzhen, China.
Front Digit Health. 2024 Dec 13;6:1506071. doi: 10.3389/fdgth.2024.1506071. eCollection 2024.
The COVID-19 pandemic has placed unprecedented strain on healthcare systems, mainly due to the highly variable and challenging to predict patient length of stay (LOS). This study aims to identify the primary factors impacting LOS for patients before and during the COVID-19 pandemic.
This study collected electronic medical record data from Zhongnan Hospital of Wuhan University. We employed six machine learning algorithms to predict the probability of LOS.
After implementing variable selection, we identified 35 variables affecting the LOS for COVID-19 patients to establish the model. The top three predictive factors were out-of-pocket amount, medical insurance, and admission deplanement. The experiments conducted showed that XGBoost (XGB) achieved the best performance. The MAE, RMSE, and MAPE errors before and during the COVID-19 pandemic are lower than 3% on average for household registration in Wuhan and non-household registration in Wuhan.
Research finds machine learning is reasonable in predicting LOS before and during the COVID-19 pandemic. This study offers valuable guidance to hospital administrators for planning resource allocation strategies that can effectively meet the demand. Consequently, these insights contribute to improved quality of care and wiser utilization of scarce resources.
新冠疫情给医疗系统带来了前所未有的压力，主要原因是患者住院时长高度可变且难以预测。本研究旨在确定新冠疫情之前及期间影响患者住院时长的主要因素。
本研究收集了武汉大学中南医院的电子病历数据。我们采用六种机器学习算法来预测住院时长的概率。
在进行变量选择后，我们确定了35个影响新冠患者住院时长的变量以建立模型。前三大预测因素是自付金额、医疗保险和入院下机。进行的实验表明，极端梯度提升（XGB）表现最佳。新冠疫情之前及期间，武汉市户籍和非武汉市户籍的平均绝对误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）均低于3%。
研究发现机器学习在预测新冠疫情之前及期间的住院时长方面是合理的。本研究为医院管理人员规划资源分配策略提供了有价值的指导，这些策略能够有效满足需求。因此，这些见解有助于提高护理质量并更明智地利用稀缺资源。
