Predicting Patient Waiting Time in the Queue System Using Deep Learning Algorithms in the Emergency Room

Hassan Hijry, Richard Olawoyin
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引用次数: 3

Abstract

Many hospitals consider the length of time waiting in queue to be a measure of emergency room (ER) overcrowding. Long waiting times plague many ER departments, hindering the ability to effectively provide medical attention to those in need and increasing overall costs. Advanced techniques such as machine learning and deep learning (DL) have played a central role in queuing system applications. This study aims to apply DL algorithms for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). We applied four optimization algorithms, including SGD, Adam, RMSprop, and AdaGrad. The algorithms were compared to find the best model with the lowest mean absolute error (MAE). A traditional mathematical simulation was used for additional comparisons. The results showed that the DL model is applicable using the SGD algorithm by activating a lowest MAE of 10.80 minutes (24% error reduction) to predict patients' waiting times. This work presents a theoretical contribution of predicting patients’ waiting time with alternative techniques by achieving the highest performing model to better prioritize patients waiting in the queue. Also, this study offers a practical contribution by using real-life data from ERs. Furthermore, we proposed models to predict patients' waiting time with more accurate results than a traditional mathematical method. Our approach can be easily implemented for the queue system in the healthcare sector using electronic health records (EHR) data.
基于深度学习算法的急诊室排队系统患者等待时间预测
许多医院认为排队等候的时间长度是衡量急诊室(ER)拥挤程度的一个指标。漫长的等待时间困扰着许多急诊室,阻碍了有效地为有需要的人提供医疗服务的能力,并增加了总体成本。机器学习和深度学习(DL)等先进技术在排队系统应用中发挥了核心作用。本研究旨在将深度学习算法应用于历史排队变量,以预测系统中患者的等待时间,或者代替排队理论(QT)。我们采用了SGD、Adam、RMSprop和AdaGrad四种优化算法。通过对算法的比较,找出具有最低平均绝对误差(MAE)的最佳模型。使用传统的数学模拟进行额外的比较。结果表明,DL模型适用于SGD算法,激活最低MAE为10.80分钟(误差降低24%)来预测患者的等待时间。这项工作提出了预测患者的等待时间与替代技术的理论贡献,通过实现最高性能的模型,以更好地优先考虑患者排队等待。此外,本研究通过使用急诊室的真实数据提供了实际贡献。此外,我们提出了预测患者等待时间的模型,其结果比传统的数学方法更准确。我们的方法可以很容易地在医疗保健部门使用电子健康记录(EHR)数据的队列系统中实现。
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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