Wu Weiming, Li Min, Jiang Huilin, Sun Min, Zhu Yongcheng, Zhu Gongxu, Li Yanling, Li Yunmei, Mo Junrong, Chen Xiaohui, Mao Haifeng
1Emergency Department, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, China.
2Goodwill Hessian Health Technology Co., Ltd., Beijing 100007, China.
World J Emerg Med. 2025 May 1;16(3):220-224. doi: 10.5847/wjem.j.1920-8642.2025.048.
The problem of prolonged emergency department length of stay (EDLOS) is becoming increasingly crucial. This study aims to develop a machine learning (ML) model to predict EDLOS, with EDLOS as the outcome variable and demographic characteristics, triage level, and medical resource utilization as predictive factors.
A retrospective analysis was performed on the patients who visited the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to September 2021, and a total of 321,012 cases were identified. According to the inclusion and exclusion criteria, 187,028 cases were finally included in the analysis. ML analysis was performed using R-squared (R), and the predictive factors and the EDLOS were used as independent variables and dependent variables, respectively, to establish models. The performance evaluation of the ML models was conducted through the utilization of the mean absolute error (MAE), root mean square error (RMSE), and R, enabling an objective comparative analysis.
In the comparative analysis of the six ML models, light gradient boosting machine (LightGBM) model demonstrated the lowest MAE (443.519) and RMSE (826.783), and the highest R² value (0.48), indicating better model fit and predictive performance. Among the top 10 predictive factors associated with EDLOS according to the LightGBM model, the emergency waiting time, age, and emergency arrival time had the most significant impact on the EDLOS.
The LightGBM model suggests that the emergency waiting time, age, and emergency arrival time may be used to predict the EDLOS.
急诊科住院时间延长的问题日益关键。本研究旨在开发一种机器学习（ML）模型来预测急诊科住院时间，将急诊科住院时间作为结果变量，人口统计学特征、分诊级别和医疗资源利用作为预测因素。
对2019年3月至2021年9月在广州医科大学附属第二医院急诊科就诊的患者进行回顾性分析，共识别出321,012例病例。根据纳入和排除标准，最终纳入187,028例病例进行分析。使用R平方（R）进行ML分析，分别将预测因素和急诊科住院时间作为自变量和因变量来建立模型。通过利用平均绝对误差（MAE）、均方根误差（RMSE）和R对ML模型进行性能评估，从而进行客观的比较分析。
在六个ML模型的比较分析中，轻梯度提升机（LightGBM）模型的MAE最低（443.519），RMSE最低（826.783），R²值最高（0.48），表明模型拟合度和预测性能更好。根据LightGBM模型，在与急诊科住院时间相关的前10个预测因素中，急诊等待时间、年龄和急诊到达时间对急诊科住院时间的影响最为显著。
LightGBM模型表明，急诊等待时间、年龄和急诊到达时间可用于预测急诊科住院时间。
