Motivation

The timely identification of patients for hospitalization in emergency departments (EDs) can facilitate efficient use of hospital resources. Machine learning can help the early prediction of ED disposition; however, application of machine learning models requires both computer science skills and domain knowledge. This presents a barrier for those who want to apply machine learning to real-world settings.

Objectives

The objective of this study was to construct a competitive predictive model with a minimal amount of human effort to facilitate decisions regarding hospitalization of patients.

Methods

This study used the electronic health record data from five EDs in a single healthcare system, including an academic urban children’s hospital ED, from January 2009 to December 2013. We constructed two machine learning models by using automated machine learning algorithm (autoML) which allows non-experts to use machine learning model: one with data only available at ED triage, the other adding information available one hour into the ED visit. Random forest and logistic regression were employed as bench-marking models. The ratio of the training dataset to the test dataset was 8:2, and the area under the receiver operating characteristic curve (AUC), accuracy, and F1 were calculated to assess the quality of the models.

Results

Of the 9,069 ED visits analyzed, the study population was made up of males (62.7 %), median (IQR) age was 6 (4, 10) years, and public insurance coverage (66.0 %). The majority had an Emergency Severity Index score of 3 (52.9 %). The prevalence of hospitalization was 22.5 %. The AUCs were 0.914 and 0.942. AUCs were 0.831 and 0.886 for random forests, and 0.795 and 0.823 for logistic regression. Among the predictors, an outcome at a prior visit, ESI level, time to first medication, and time to triage were the most important features for the prediction of the need for hospitalization.

Conclusions

In comparison with the conventional approaches, the use of autoML improved the predictive ability for the need for hospitalization. The findings can optimize ED management, hospital-level resource utilization and improve quality. Furthermore, this approach can support the design of a more effective patient ED flow for pediatric asthma care.

中文翻译：

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使用机器学习预测急诊科的小儿哮喘患者的住院

动机

及时确定要在急诊科住院的患者可以促进医院资源的有效利用。机器学习可以帮助早期预测ED的病情。但是，机器学习模型的应用需要计算机科学技能和领域知识。这为想要将机器学习应用于实际设置的人提供了障碍。

目标

这项研究的目的是构建一种竞争性的预测模型，以最少的人力来促进有关患者住院的决策。

方法

这项研究在2009年1月至2013年12月期间，在一个医疗保健系统（包括一个学术性的城市儿童医院急诊室）中使用了来自五个急诊室的电子健康记录数据。我们使用自动机器学习算法（autoML）构建了两个机器学习模型，该模型允许-使用机器学习模型的专家：一个仅在ED分类中提供数据，另一个在ED访问一小时后添加信息。采用随机森林和逻辑回归作为基准分析模型。训练数据集与测试数据集的比率为8：2，并计算接收器工作特征曲线（AUC），准确性和F1下的面积以评估模型的质量。

结果

在所分析的9,069例ED访视中，研究人群由男性（62.7％），中位（IQR）年龄为6（4、10）岁和公共保险覆盖率（66.0％）组成。多数患者的紧急度指数为3（52.9％）。住院患病率为22.5％。AUC为0.914和0.942。随机森林的AUC分别为0.831和0.886，逻辑回归的AUC为0.795和0.823。在这些预测因素中，事先就诊的结局，ESI水平，首次用药时间和分诊时间是预测住院需求的最重要特征。

结论

与传统方法相比，autoML的使用提高了住院需求的预测能力。这些发现可以优化急诊管理，改善医院一级的资源利用率并提高质量。此外，这种方法可以支持设计更有效的用于小儿哮喘护理的患者ED流程。