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首页> 外文期刊>Expert Systems with Application >Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department
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Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department

机译：基于改进遗传算法的特征选择与预训练深层神经网络相结合的门诊需求预测

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摘要

A well-performed demand forecasting can provide outpatient department (OPD) managers with essential information for staff scheduling and rostering, considering the non-reservation policy of OPD in China. Based on the results reported by relevant studies, most approaches have focused on forecasting the overall amount of patient flow and ignored the demand for other key resources in OPD or similar department. Moreover, few studies have conducted feature selection before training a forecast model, which is a significant pre-processing operation of data mining and widely applied for knowledge discovery in expert and intelligent system. This study develops a novel hybrid methodology to forecast the patients' demand for different key resources in OPD, by combining a new feature selection method and a deep learning approach. A modified version of genetic algorithm (MGA) is proposed for feature selection. The key operators of normal genetic algorithm are redesigned to utilize useful information provided by filter-based feature selection and feature combinations. A feedforward deep neural network is introduced as the forecast model, and the initial parameter set is generated from a stacked autoencoder-based pre-training process to overcome the optimization challenges in constructing deep architectures. In order to evaluate the performance of our methodology, it is applied to an OPD located at Northeast China. The results are compared with those obtained from combinations of other feature selection methods and demand forecasting models. Compared with GA and PCA, MGA improves the quality and efficiency of feature selection, with less selected features to get higher forecast accuracy. Pre-trained DNN optimally strengthens the advantage of MGA, compared with MLR, ARIMAX and SANN. The combination of MGA and pre-trained DNN possesses strongest predictive power among all involved combinations. Furthermore, the results of proposed methodology are crucial prerequisites for staff scheduling and resource allocation in OPD. Elite features obtained by MGA can provide practical insights on potential association between manifold feature combinations and demand variance. (C) 2017 Elsevier Ltd. All rights reserved.
机译：考虑到OPD在中国的非保留政策，执行良好的需求预测可以为门诊（OPD）经理提供必要的信息，以进行人员安排和排班。根据相关研究报告的结果，大多数方法侧重于预测患者流量的总量，而忽略了OPD或类似部门对其他关键资源的需求。此外，很少有研究在训练预测模型之前进行特征选择，这是数据挖掘的重要预处理操作，并广泛应用于专家和智能系统中的知识发现。这项研究通过结合新的特征选择方法和深度学习方法，开发了一种新颖的混合方法来预测患者对OPD中不同关键资源的需求。提出了遗传算法（MGA）的改进版本用于特征选择。重新设计了常规遗传算法的关键运算符，以利用基于过滤器的特征选择和特征组合所提供的有用信息。引入前馈深度神经网络作为预测模型，并从基于自动编码器的堆栈式预训练过程生成初始参数集，以克服构建深度架构时的优化挑战。为了评估我们方法的性能，将其应用于位于中国东北的OPD。将结果与从其他特征选择方法和需求预测模型的组合获得的结果进行比较。与GA和PCA相比，MGA提高了特征选择的质量和效率，而较少选择的特征可以提高预测精度。与MLR，ARIMAX和SANN相比，预训练的DNN可以最佳地增强MGA的优势。在所有涉及的组合中，MGA和预训练的DNN的组合具有最强的预测能力。此外，拟议方法的结果是OPD人员调度和资源分配的关键前提。 MGA获得的精英功能可以提供有关流水线特征组合和需求差异之间潜在关联的实用见解。 （C）2017 Elsevier Ltd.保留所有权利。

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