Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care. Electrocardiogram (ECG) signals are fundamental for evaluating a patient's cardiac health and are readily available. Despite this, little attention has been given to the remarkable potential of ECG data in detecting non-cardiac conditions. In our study, we used publicly available datasets (MIMIC-IV-ECG-ICD and ECG-VIEW II) to investigate the feasibility of inferring general diagnostic conditions from ECG features. To this end, we trained a tree-based model (XGBoost) based on ECG features and basic demographic features to estimate a wide range of diagnoses, encompassing both cardiac and non-cardiac conditions. Our results demonstrate the reliability of estimating 23 cardiac as well as 21 non-cardiac conditions above 0.7 AUROC in a statistically significant manner across a wide range of physiological categories. Our findings underscore the predictive potential of ECG data in identifying well-known cardiac conditions. However, even more striking, this research represents a pioneering effort in systematically expanding the scope of ECG-based diagnosis to conditions not traditionally associated with the cardiac system.

翻译：确保及时、准确地诊断疾病对于有效患者护理至关重要。心电图（ECG）信号是评估患者心脏健康的基础，且易于获取。尽管如此，ECG数据在检测非心脏疾病方面的巨大潜力却鲜受关注。在本研究中，我们利用公开数据集（MIMIC-IV-ECG-ICD 和 ECG-VIEW II）探究了从ECG特征推断一般诊断状况的可行性。为此，我们基于ECG特征和基本人口统计学特征训练了一个树模型（XGBoost），以评估涵盖心脏与非心脏疾病的广泛诊断范围。我们的结果表明，在统计学显著水平上，能够可靠地评估23种心脏疾病和21种非心脏疾病，其AUROC值均超过0.7，覆盖广泛的生理学类别。这些发现凸显了ECG数据在识别已知心脏疾病方面的预测潜力。然而，更引人注目的是，本研究开创性地将基于ECG的诊断范围系统性地扩展至传统上与心脏系统无关的疾病领域。