背景:急诊科(ED)分诊系统的开发在准确区分急性腹痛(AAP)患者方面仍然具有挑战性,这些患者由于主观性和局限性而急需手术。我们使用机器学习模型来预测急诊外科腹痛患者的分诊,然后将它们的性能与传统的Logistic回归模型进行比较。
方法:选取2014年3月1日至2022年3月1日武汉大学中南医院收治的38.214例急性腹痛患者,确定所有成年患者(≥18岁)。我们利用电子病历中常规可用的分诊数据作为预测因子,包括结构化数据(例如,分诊生命体征,性别,和年龄)和非结构化数据(自由文本格式的主要投诉和体检)。主要结果指标是是否进行了急诊手术。数据集是随机抽样的,80%分配给训练集,20%分配给测试集。我们开发了5种机器学习模型:光梯度升压机(LightGBM),极限梯度提升(XGBoost),深度神经网络(DNN)和随机森林(RF)。Logistic回归(LR)作为参考模型。计算了每个模型的模型性能,包括接受者-工作特征曲线(AUC)和净收益(决策曲线)下的面积,以及混乱矩阵。
结果:在所有38.214例急性腹痛患者中,4208例接受了紧急腹部手术,而34.006例接受了非手术治疗。在手术结果预测中,所有4个机器学习模型的性能都优于参考模型(例如,AUC,光GBM中的0.899[95CI0.891-0.903]与0.885[95CI0.876-0.891]在参考模型中),同样,与参考模型相比,大多数机器学习模型在网络重分类方面表现出显着改进(例如,XGBoost中的NRI为0.0812[95CI,0.055-0.1105]),RF模型除外。决策曲线分析表明,在整个阈值范围内,XGBoost和LightGBM模型的净收益高于参考模型。特别是,LightGBM模型在预测紧急腹部手术需求方面表现良好,灵敏度更高,特异性,和准确性。
结论:与传统模型相比,机器学习模型在预测紧急腹痛手术方面表现出优异的性能。现代机器学习改善了临床分诊决策,并确保急需的患者获得优先的紧急资源和及时,有效治疗。
BACKGROUND: The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use machine learning models to predict emergency surgical abdominal pain patients in triage, and then compare their performance with conventional Logistic regression models.
METHODS: Using 38 214 patients presenting with acute abdominal pain at Zhongnan Hospital of Wuhan University between March 1, 2014, and March 1, 2022, we identified all adult patients (aged ≥18 years). We utilized routinely available triage data in electronic medical records as predictors, including structured data (eg, triage vital signs, gender, and age) and unstructured data (chief complaints and physical examinations in free-text format). The primary outcome measure was whether emergency surgery was performed. The dataset was randomly sampled, with 80% assigned to the training set and 20% to the test set. We developed 5 machine learning models: Light Gradient Boosting Machine (Light GBM), eXtreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Random Forest (RF). Logistic regression (LR) served as the reference model. Model performance was calculated for each model, including the area under the receiver-work characteristic curve (AUC) and net benefit (decision curve), as well as the confusion matrix.
RESULTS: Of all the 38 214 acute abdominal pain patients, 4208 underwent emergency abdominal surgery while 34 006 received non-surgical treatment. In the surgery outcome prediction, all 4 machine learning models outperformed the reference model (eg, AUC, 0.899 [95%CI 0.891-0.903] in the Light GBM vs. 0.885 [95%CI 0.876-0.891] in the reference model), Similarly, most machine learning models exhibited significant improvements in net reclassification compared to the reference model (eg, NRIs of 0.0812[95%CI, 0.055-0.1105] in the XGBoost), with the exception of the RF model. Decision curve analysis shows that across the entire range of thresholds, the net benefits of the XGBoost and the Light GBM models were higher than the reference model. In particular, the Light GBM model performed well in predicting the need for emergency abdominal surgery with higher sensitivity, specificity, and accuracy.
CONCLUSIONS: Machine learning models have demonstrated superior performance in predicting emergency abdominal pain surgery compared to traditional models. Modern machine learning improves clinical triage decisions and ensures that critically needy patients receive priority for emergency resources and timely, effective treatment.