通过将机器学习方法应用于肥厚型心肌病患者的电子健康记录来识别室性心律失常及其预测因素（HCM-VAr-Risk模型）
Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy(HCM-VAr-Risk Model)
摘要：
肥大性心肌病（HC）心脏性猝死（SCD）的临床风险分层采用源自美国心脏病学会基金会/美国心脏协会（ACCF/AHA）指南或HCM风险-SCD模型（C指数为0.69）的规则，其中利用了一些临床变量。我们评估了考虑更广泛变量的数据驱动机器学习方法是否可以有效识别导致SCD的室性心律失常（VAr）的HC患者。我们扫描了711例HC患者的电子健康记录，以检查持续性室性心动过速或室颤。室性心动过速或室颤患者（n = 61）被标记为VAr病例，其余患者（n = 650）被标记为非VAr。使用双样本t检验和信息增益标准来识别区分VAr和非VAr的最具信息性的临床变量;患者记录减少到仅包括这些变量。通过采用过采样和欠采样相结合的策略，解决了由于VAr案例数量少而导致的数据不平衡问题。我们在这种采样方法下训练和测试了多个分类器，显示了有效的分类。我们评估了93个临床变量，其中22个被证明预测VAr。基于这22个变量训练并校正数据不平衡的逻辑回归和朴素贝叶斯分类器的集合在分离VAr与非VAr病例中最有效（灵敏度= 0.73，特异性= 0.76，C指数= 0.83）。我们的方法（HCM-VAr风险模型）确定了12个新的预测VAr，除了10个既定的SCD预测。总之，这是机器学习首次应用于使用临床属性识别患有VAr的HC患者。
英文摘要：
Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiology Foundation/American Heart Association (ACCF/AHA) guidelines or the HCM Risk-SCD model (C-index of 0.69), which utilize a few clinical variables. We assessed whether data-driven machine learning methods that consider a wider range of variables can effectively identify HC patients with ventricular arrhythmias (VAr) that lead to SCD. We scanned the electronic health records of 711 HC patients for sustained ventricular tachycardia or ventricular fibrillation. Patients with ventricular tachycardia or ventricular fibrillation (n = 61) were tagged as VAr cases and the remaining (n = 650) as non-VAr. The 2-sample t test and information gain criterion were used to identify the most informative clinical variables that distinguish VAr from non-VAr; patient records were reduced to include only these variables. Data imbalance stemming from low number of VAr cases was addressed by applying a combination of over- and under-sampling strategies.We trained and tested multiple classifiers under this sampling approach, showing effective classification. We evaluated 93 clinical variables, of which 22 proved predictive of VAr. The ensemble of logistic regression and naive Bayes classifiers, trained based on these 22 variables and corrected for data imbalance, was most effective in separating VAr from non-VAr cases (sensitivity = 0.73, specificity = 0.76, C-index = 0.83). Our method (HCM-VAr-Risk Model) identified 12 new predictors of VAr, in addition to 10 established SCD predictors. In conclusion, this is the first application of machine learning for identifying HC patients with VAr, using clinical attributes.
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