论文标题
用于预测ST段升高心肌梗死患者院内死亡率的机器学习算法
Machine Learning Algorithms for Predicting in-Hospital Mortality in Patients with ST-Segment Elevation Myocardial Infar
论文作者
论文摘要
急性心肌梗塞（AMI）是冠状动脉疾病最严重的表现之一。 ST段海拔心肌梗塞（STEMI）是最严重的AMI类型。我们建议根据电子病历（HPEMR）的首页开发机器学习算法，以预测早期STEMI患者的住院死亡率。方法：这项观察性研究应用了2013年至2017年之间从中国深圳的7家三级医院收集的临床信息。患者的STEMI数据用于训练4种不同的机器学习算法，以预测STEMI患者的住院死亡率，包括逻辑回归，支持矢量机，梯度增强决策树和人工神经元网络。结果：总共有5865例STEMI患者参加了我们的研究。该模型是通过考虑3种类型的变量来开发的，其中包括人口统计数据，诊断和合并症以及HPEMR上的住院信息。报告了使用单变量逻辑回归的选定特征的关联。 Specially, for the comorbidities, atrial fibrillation (OR: 11.0; 95% CI: 5.64 - 20.2), acute renal failure (OR: 9.75; 95% CI: 3.81 - 25.0), type 2 diabetic nephropathy (OR: 5.45; 95% CI: 1.57 - 19.0), acute heart failure (OR: 6.05; 95% CI: 1.99-14.9）和心脏功能IV级（OR：28.6; 95％CI：20.6-39.6）被发现与高几率死亡相关。在测试数据集中，我们的模型显示出良好的歧视能力，如接收器操作特征曲线下的面积（AUC； 0.879）（95％CI：0.825-0.933）。
Acute myocardial infarction (AMI) is one of the most severe manifestation of coronary artery disease. ST-segment elevation myocardial infarction (STEMI) is the most serious type of AMI. We proposed to develop a machine learning algorithm based on the home page of electronic medical record (HPEMR) for predicting in-hospital mortality of patients with STEMI in the early stage. Methods: This observational study applied clinical information collected between 2013 and 2017 from 7 tertiary hospitals in Shenzhen, China. The patients' STEMI data were used to train 4 different machine learning algorithms to predict in-hospital mortality among the patients with STEMI, including Logistic Regression, Support Vector Machine, Gradient Boosting Decision Tree, and Artificial Neuron network. Results: A total of 5865 patients with STEMI were enrolled in our study. The model was developed by considering 3 types of variables, which included demographic data, diagnosis and comorbidities, and hospitalization information basing on HPEMR. The association of selected features using univariant logistic regression was reported. Specially, for the comorbidities, atrial fibrillation (OR: 11.0; 95% CI: 5.64 - 20.2), acute renal failure (OR: 9.75; 95% CI: 3.81 - 25.0), type 2 diabetic nephropathy (OR: 5.45; 95% CI: 1.57 - 19.0), acute heart failure (OR: 6.05; 95% CI: 1.99 - 14.9), and cardiac function grade IV (OR: 28.6; 95% CI: 20.6 - 39.6) were found to be associated with a high odds of death. Within the test dataset, our model showed a good discrimination ability as measured by area under the receiver operating characteristic curve (AUC; 0.879) (95% CI: 0.825 - 0.933).
