Development of an emergency department length-of-stay prediction model based on machine learning.

IF 2.6 3区 医学 Q1 EMERGENCY MEDICINE
Weiming Wu, Min Li, Huilin Jiang, Min Sun, Yongcheng Zhu, Gongxu Zhu, Yanling Li, Yunmei Li, Junrong Mo, Xiaohui Chen, Haifeng Mao
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Abstract

Background: 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.

Methods: 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 (R2), 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 R2, enabling an objective comparative analysis.

Results: 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.

Conclusion: The LightGBM model suggests that the emergency waiting time, age, and emergency arrival time may be used to predict the EDLOS.

基于机器学习的急诊科住院时间预测模型的开发。
背景:急诊科住院时间延长(EDLOS)的问题变得越来越重要。本研究旨在建立一个机器学习(ML)模型来预测EDLOS,以EDLOS为结果变量,以人口统计学特征、分诊水平和医疗资源利用率为预测因素。方法:回顾性分析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,共鉴定出321,012例。根据纳入和排除标准,最终纳入187028例。采用r²(R2)进行ML分析,分别以预测因素和EDLOS作为自变量和因变量建立模型。利用平均绝对误差(MAE)、均方根误差(RMSE)和R2对ML模型进行性能评价,进行客观的比较分析。结果:在6种ML模型的对比分析中,光梯度增强机(LightGBM)模型的MAE(443.519)和RMSE(826.783)最低,R²值(0.48)最高,表明模型拟合和预测性能较好。根据LightGBM模型,在与EDLOS相关的前10个预测因素中,紧急等待时间、年龄和紧急到达时间对EDLOS的影响最为显著。结论:LightGBM模型提示急诊等待时间、年龄和急诊到达时间可用于预测EDLOS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.50
自引率
28.60%
发文量
671
期刊介绍: The journal will cover technical, clinical and bioengineering studies related to multidisciplinary specialties of emergency medicine, such as cardiopulmonary resuscitation, acute injury, out-of-hospital emergency medical service, intensive care, injury and disease prevention, disaster management, healthy policy and ethics, toxicology, and sudden illness, including cardiology, internal medicine, anesthesiology, orthopedics, and trauma care, and more. The journal also features basic science, special reports, case reports, board review questions, and more. Editorials and communications to the editor explore controversial issues and encourage further discussion by physicians dealing with emergency medicine.
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