Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

被引：1
|
作者：
Zheng, ZhiChang [1 ,2 ]
Guo, Ruifeng [1 ]
Wang, Nian [3 ]
Jiang, Bo [3 ]
Ma, Chun Peng [4 ]
Ai, Hui [1 ]
Wang, Xiao [1 ]
Nie, ShaoPing [1 ]
机构：
[1] Capital Med Univ, Beijing Anzhen Hosp, Ctr Coronary Artery Dis, Div Cardiol, Anzhen Rd Chaoyang Dist, Beijing 100029, Peoples R China
[2] Capital Med Univ, Beijing Boai Hosp, China Rehabil Res Ctr, Dept Cardiol, 10 Jiaomen North Rd, Beijing 100068, Peoples R China
[3] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China
[4] Hebei Med Univ, Hosp Qinhuangdao 1, Dept Cardiol, Qinhuangdao 066099, Hebei, Peoples R China
来源：
JOURNAL OF HEALTHCARE ENGINEERING | 2022年 / 2022卷
基金：
中国国家自然科学基金;
关键词：
Forecasting;
D O I：
10.1155/2022/1795588
中图分类号：
R19 [保健组织与事业（卫生事业管理）];
学科分类号：
摘要：
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.
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收藏
页数：7
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