运用机器学习预测急诊科胸痛患者的血运重建需求。
Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department.
机构信息
Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road Chaoyang District, Beijing 100029, China.
Department of Cardiology, Beijing Bo'ai Hospital, China Rehabilitation Research Center, Capital Medical University, No. 10 Jiaomen North Road Fengtai District, Beijing 100068, China.
出版信息
J Healthc Eng. 2022 Apr 14;2022:1795588. doi: 10.1155/2022/1795588. eCollection 2022.
OBJECTIVE
The study aimed to use machine learning algorithms to predict the need for revascularization in patients presenting with chest pain in the emergency department.
METHODS
We obtained data from 581 patients with chest pain, 264 who underwent revascularization, and the other 317 were treated with medication alone for 3 months. Using standard algorithms, linear discriminant analysis, and standard algorithms, we analyzed 41 features relevant to coronary artery disease (CAD).
RESULTS
We identified seven robust predictive features. The combination of these predictors gave an area under the curve (AUC) of 0.830 to predict the need for revascularization. By contrast, the GRACE score gave an AUC of 0.68.
CONCLUSIONS
This machine learning-based approach predicts the need for revascularization in patients with chest pain.
目的
本研究旨在使用机器学习算法预测急诊科胸痛患者的血运重建需求。
方法
我们从 581 名胸痛患者中获取数据，其中 264 名患者接受了血运重建，另外 317 名患者仅接受了 3 个月的药物治疗。使用标准算法、线性判别分析和标准算法分析了 41 个与冠心病（CAD）相关的特征。
结果
我们确定了七个稳健的预测特征。这些预测因子的组合预测血运重建需求的曲线下面积（AUC）为 0.830，而 GRACE 评分的 AUC 为 0.68。
结论
这种基于机器学习的方法可预测胸痛患者血运重建的需求。
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