Industrial & Transportation Engineering Department, Universidade Federal do Rio Grande do Sul - UFRGS, 90035-190, Porto Alegre, RS, Brazil.
School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 136-713, Republic of Korea.
Artif Intell Med. 2018 Jan;84:23-33. doi: 10.1016/j.artmed.2017.10.002. Epub 2017 Oct 18.
Long length of stay and overcrowding in emergency departments (EDs) are two common problems in the healthcare industry. To decrease the average length of stay (ALOS) and tackle overcrowding, numerous resources, including the number of doctors, nurses and receptionists need to be adjusted, while a number of constraints are to be considered at the same time. In this study, an efficient method based on agent-based simulation, machine learning and the genetic algorithm (GA) is presented to determine optimum resource allocation in emergency departments. GA can effectively explore the entire domain of all 19 variables and identify the optimum resource allocation through evolution and mimicking the survival of the fittest concept. A chaotic mutation operator is used in this study to boost GA performance. A model of the system needs to be run several thousand times through the GA evolution process to evaluate each solution, hence the process is computationally expensive. To overcome this drawback, a robust metamodel is initially constructed based on an agent-based system simulation. The simulation exhibits ED performance with various resource allocations and trains the metamodel. The metamodel is created with an ensemble of the adaptive neuro-fuzzy inference system (ANFIS), feedforward neural network (FFNN) and recurrent neural network (RNN) using the adaptive boosting (AdaBoost) ensemble algorithm. The proposed GA-based optimization approach is tested in a public ED, and it is shown to decrease the ALOS in this ED case study by 14%. Additionally, the proposed metamodel shows a 26.6% improvement compared to the average results of ANFIS, FFNN and RNN in terms of mean absolute percentage error (MAPE).
长住院时间和急诊科（ED）过度拥挤是医疗保健行业的两个常见问题。为了缩短平均住院时间（ALOS）并解决过度拥挤问题，需要调整包括医生、护士和接待员数量在内的大量资源，同时需要考虑许多限制因素。在这项研究中，提出了一种基于基于代理的仿真、机器学习和遗传算法（GA）的有效方法，用于确定急诊科的最佳资源分配。GA 可以有效地探索所有 19 个变量的整个领域，并通过进化和模拟适者生存的概念来识别最佳资源分配。在这项研究中，使用混沌突变算子来提高 GA 的性能。需要通过 GA 进化过程运行系统模型数千次来评估每个解决方案，因此该过程计算成本很高。为了克服这一缺点，最初基于基于代理的系统仿真构建了一个稳健的元模型。该仿真展示了具有各种资源分配的 ED 性能，并对元模型进行了训练。元模型是使用自适应神经模糊推理系统 (ANFIS)、前馈神经网络 (FFNN) 和递归神经网络 (RNN) 的集成使用自适应增强 (AdaBoost) 集成算法创建的。所提出的基于 GA 的优化方法在公共 ED 中进行了测试，结果表明在该 ED 案例研究中，ALOS 缩短了 14%。此外，与 ANFIS、FFNN 和 RNN 的平均结果相比，所提出的元模型在平均绝对百分比误差 (MAPE) 方面显示出 26.6%的改进。
