用心电图数据训练模型，可无创识别肝病，效果可靠且结果可解释。
Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach
- 基于心电图特征的树模型，通过机器学习识别肝病。
- 外验证中酒精性肝病预测准确率达76.4%，肝衰竭达75.0%。
- 发现年龄和QTc间期是关键指标，适合临床筛查与早期预警。
肝病是全球重大健康挑战，常需昂贵且侵入性诊断。心电图（ECG）作为广泛可用的无创工具，可通过捕捉心血管-肝脏交互作用实现肝病检测。本研究在两个大规模数据集MIMIC-IV-ECG（467,729例，2008–2019年）和ECG-View II（775,535例，1994–2013年）上训练树基机器学习模型，以心电图特征检测肝病，任务为二分类，性能通过受试者工作特征曲线下面积（AUROC）评估。为提升可解释性，采用可解释性方法识别关键预测特征。结果显示，模型具备强预测能力且具有良好泛化性：例如，酒精性肝病（K70）内部AUROC为0.8025（95%置信区间，0.8020–0.8035），外部验证为0.7644（95%置信区间，0.7641–0.7649）；肝衰竭（K72）分别为0.7404（95%置信区间，0.7389–0.7415）和0.7498（95%置信区间，0.7494–0.7509）。可解释性分析一致识别出年龄和延长的QTc间期（校正后QT，反映心室复极）为关键预测因子。与自主神经调节及电传导异常相关的特征也显著突出，支持已知的心血管-肝脏关联，并提示QTc可能成为潜在生物标志物。该方法为资源有限地区提供了一种有前景的无创肝病筛查路径，揭示临床相关生物标志物，支持早期检测、风险分层及后续靶向临床评估。
原文摘要 · Abstract (English)
Background: Liver diseases present a significant global health challenge and often require costly, invasive diagnostics. Electrocardiography (ECG), a widely available and non-invasive tool, can enable the detection of liver disease by capturing cardiovascular-hepatic interactions. Methods: We trained tree-based machine learning models on ECG features to detect liver diseases using two large datasets: MIMIC-IV-ECG (467,729 patients, 2008-2019) and ECG-View II (775,535 patients, 1994-2013). The task was framed as binary classification, with performance evaluated via the area under the receiver operating characteristic curve (AUROC). To improve interpretability, we applied explainability methods to identify key predictive features. Findings: The models showed strong predictive performance with good generalizability. For example, AUROCs for alcoholic liver disease (K70) were 0.8025 (95% confidence interval (CI), 0.8020-0.8035) internally and 0.7644 (95% CI, 0.7641-0.7649) externally; for hepatic failure (K72), scores were 0.7404 (95% CI, 0.7389-0.7415) and 0.7498 (95% CI, 0.7494-0.7509), respectively. The explainability analysis consistently identified age and prolonged QTc intervals (corrected QT, reflecting ventricular repolarization) as key predictors. Features linked to autonomic regulation and electrical conduction abnormalities were also prominent, supporting known cardiovascular-liver connections and suggesting QTc as a potential biomarker. Interpretation: ECG-based machine learning offers a promising, interpretable approach for liver disease detection, particularly in resource-limited settings. By revealing clinically relevant biomarkers, this method supports non-invasive diagnostics, early detection, and risk stratification prior to targeted clinical assessments.
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