Background Early diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, electrocardiogram (ECG)-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values (PPVs) to facilitate meaningful recommendations for echocardiography.

中文翻译：

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rECHOmmend：一种基于心电图的机器学习方法，用于识别超声心动图可检测到的未确诊结构性心脏病的高风险患者

背景结构性心脏病的早期诊断可改善患者的预后，但仍有许多未被确诊。虽然使用超声心动图进行人群筛查不切实际，但基于心电图 (ECG) 的预测模型可以帮助定位高危患者。我们开发了一种新的基于 ECG 的机器学习方法来预测多种结构性心脏病，假设复合模型会产生更高的患病率和阳性预测值 (PPV)，以促进对超声心动图的有意义的建议。