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使用特征工程方法和机器学习,对急诊室患者到达的预测得到了改进

Bruno Matos Porto^1,2, Flavio Sanson Fogliatto^3,4

¹Industrial Engineering Department, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5th floor, Porto Alegre, RS, 90020-035, Brazil. bmatosporto@gmail.com.

BMC medical informatics and decision making

|December 19, 2024

概括

使用机器学习,特别是 eXtreme Gradient Boosting (XGBoost) 精确的急救部门 (ED) 抵达预测,提高了资源配置,减少了患者等待时间. 功能工程提高了预测准确度,以更好地管理ED.

科学领域:

机器学习应用机器学习应用
医疗保健运营研究研究研究
预测分析是一种预测分析.

背景情况:

紧急部门 (ED) 过度拥挤是影响医疗保健效率的全球性挑战.
准确的患者到达预测对于优化人员和资源配置至关重要.
本研究探讨了用于预测每日ED患者数量的新型预测因素.

研究的目的:

评估机器学习算法,用于预测每日急诊室患者的到来.
评估日历,气象和特征工程变量对预测准确性的影响.
为了比较用于ED预测的既定和新型机器学习模型的性能.

主要方法:

测试了六种机器学习算法,包括光梯度增强机 (LightGBM),带有辐射基函数的支持向量机 (SVM-RBF) 和神经网络自回归 (NNAR).
使用交叉验证的网格搜索进行了超参数调整.
预测性能使用五倍交叉验证和四个关键指标在7天和45天的预测时间范围内进行评估.

主要成果:

极端梯度增强 (XGBoost) 在两个预测视野中都表现出卓越的性能,超过了之前的研究.
在11个数据集中,XGBoost和NNAR是9个数据集的顶级模型,平均绝对百分比错误 (MAPE) 在5.03%至14.1%之间.
功能工程显著提高了机器学习算法的预测性能.

结论:

功能工程方法显著提高了预测急救部门到达的准确性.
准确的ED到达预测对于有效管理人力和物质资源至关重要.
提高预测准确度有助于减少患者的等待时间和逗留时间.

关键词:

应急部门的紧急情况.功能工程的特点工程.机器学习算法机器学习算法患者到达患者到达患者访问预测预测时间序列预测时间序列预测

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