Background

Accurate differentiation and prioritization in emergency department (ED) triage is important to identify high-risk patients and to efficiently allocate of finite resources. Using data available from patients with suspected cardiovascular disease presenting at ED triage, this study aimed to train and compare the performance of four common machine learning models to assist in decision making of triage levels.

Methods

This cross-sectional study in the second Affiliated Hospital of Guangzhou Medical University was conducted from August 2015 to December 2018 inclusive. Demographic information, vital signs, blood glucose, and other available triage scores were collected. Four machine learning models – multinomial logistic regression (multinomial LR), eXtreme gradient boosting (XGBoost), random forest (RF) and gradient-boosted decision tree (GBDT) – were compared. For each model, 80 % of the data set was used for training and 20 % was used to test the models. The area under the receiver operating characteristic curve (AUC), accuracy and macro- $F_1$ were calculated for each model.

Results

In 17,661 patients presenting with suspected cardiovascular disease, the distribution of triage of level 1, level 2, level 3 and level 4 were 1.3 %, 18.6 %, 76.5 %, and 3.6 % respectively. The AUCs were: XGBoost (0.937), GBDT (0.921), RF (0.919) and multinomial LR (0.908). Based on feature importance generated by XGBoost, blood pressure, pulse rate, oxygen saturation, and age were the most significant variables for making decisions at triage.

Conclusion

Four machine learning models had good discriminative ability of triage. XGBoost demonstrated a slight advantage over other models. These models could be used for differential triage of low-risk patients and high-risk patients as a strategy to improve efficiency and allocation of finite resources.

中文翻译：

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基于机器学习的模型可支持对疑似心血管疾病患者进行急诊分诊的决策

背景

急诊科（ED）分诊中的准确区分和优先次序对识别高危患者和有效分配有限资源很重要。这项研究使用ED分诊中可疑心血管疾病患者的可用数据，旨在训练和比较四种常见机器学习模型的性能，以帮助进行分诊水平的决策。

方法

本研究于2015年8月至2018年12月在广州医科大学第二附属医院进行。收集人口统计信息，生命体征，血糖和其他可用的分类评分。比较了四种机器学习模型-多项式逻辑回归（多项式LR），极限梯度提升（XGBoost），随机森林（RF）和梯度增强决策树（GBDT）。对于每个模型，将80％的数据集用于训练，并将20％的数据用于测试模型。接收器工作特性曲线（AUC），精度和宏下的区域$F_{\mathrm{1个}}$ 为每个模型计算。

结果

在17,661名怀疑患有心血管疾病的患者中，级别1，级别2，级别3和级别4的分流分布分别为1.3％，18.6％，76.5％和3.6％。AUC为：XGBoost（0.937），GBDT（0.921），RF（0.919）和多项式LR（0.908）。基于XGBoost产生的功能重要性，血压，脉搏率，血氧饱和度和年龄是在分类时做出决定的最重要变量。

结论

四种机器学习模型具有良好的判别判别能力。与其他型号相比，XGBoost表现出一点优势。这些模型可以用于低风险患者和高风险患者的区分分类，作为提高效率和分配有限资源的策略。