Hyper-parameters used in different classifiers.

BackgroundIn acute cardiovascular disease management, the delay between the admission in a hospital emergency department and the assessment of the disease from a Delayed Enhancement cardiac MRI (DE-MRI) scan is one of the barriers for an immediate management of patients with suspected myocardial infarction or myocarditis.ObjectivesThis work targets patients who arrive at the hospital with chest pain and are suspected of having a myocardial infarction or a myocarditis. The main objective is to classify these patients based solely on clinical data in order to provide an early accurate diagnosis.MethodsMachine learning (ML) and ensemble approaches have been used to construct a framework to automatically classify the patients according to their clinical conditions. 10-fold cross-validation is used during the model’s training to avoid overfitting. Approaches such as Stratified, Over-sampling, Under-sampling, NearMiss, and SMOTE were tested in order to address the imbalance of the data (i.e. proportion of cases per pathology). The ground truth is provided by a DE-MRI exam (normal exam, myocarditis or myocardial infarction).ResultsThe stacked generalization technique with Over-sampling seems to be the best one providing more than 97% of accuracy corresponding to 11 wrong classifications among 537 cases. Generally speaking, ensemble classifiers such as Stacking provided the best prediction. The five most important features are troponin, age, tobacco, sex and FEVG calculated from echocardiography.ConclusionOur study provides a reliable approach to classify the patients in emergency department between myocarditis, myocardial infarction or other patient condition from only clinical information, considering DE-MRI as ground-truth. Among the different machine learning and ensemble techniques tested, the stacked generalization technique is the best one providing an accuracy of 97.4%. This automatic classification could provide a quick answer before imaging exam such as cardiovascular MRI depending on the patient’s condition.

【背景】在急性心血管疾病的临床诊疗中，急诊入院至通过延迟增强心脏磁共振（Delayed Enhancement Cardiac MRI, DE-MRI）扫描完成病情评估的时间延迟，是疑似心肌梗死或心肌炎患者获得即时诊疗的主要障碍之一。

【研究目标】本研究针对因胸痛就诊、疑似心肌梗死或心肌炎的患者，核心目标为仅利用临床数据对患者进行分类，以实现早期精准诊断。

【研究方法】本研究采用机器学习（Machine Learning, ML）与集成学习方法构建自动分类框架，依据患者临床状态完成分类。模型训练阶段采用10折交叉验证以避免过拟合。为解决数据不平衡问题（即不同病理类型的病例占比差异），本研究测试了分层采样、过采样、欠采样、NearMiss算法与SMOTE算法等多种方案。本研究以DE-MRI检查结果作为金标准，分类结果涵盖正常检查、心肌炎或心肌梗死三类。

【研究结果】结合过采样的堆叠泛化算法表现最优，准确率超过97%，在537例病例中仅出现11例分类错误。总体而言，堆叠等集成分类器获得了最佳预测性能。本研究筛选出的五大关键特征依次为肌钙蛋白、年龄、吸烟史、性别及超声心动图检测的FEVG。

【研究结论】本研究提出了一种可靠的分类方法，仅依靠临床信息即可将急诊患者分为心肌炎、心肌梗死或其他病症三类，以DE-MRI结果作为金标准。在测试的各类机器学习与集成学习算法中，堆叠泛化算法表现最优，准确率达97.4%。该自动分类方法可在心血管MRI等影像学检查前，依据患者病情快速给出诊断参考。