用心电图和人口学信息预测胸片结果，助力资源有限地区早期筛查。
Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
- 基于心电图特征与患者信息，用可解释机器学习建模预测胸片异常。
- 模型对多种胸片发现的预测准确率可达AUROC 0.75~0.91，表现良好。
- 结果可解释，适合临床辅助决策，尤其适用于影像资源不足场景。
目的：胸部X光对肺部疾病诊断至关重要，但在资源匮乏地区常因获取受限而延误。心电图（ECG）则普遍可用、无创且常在临床流程中更早采集。本研究旨在评估心电图特征与患者人口学信息是否可用于预测胸部放射学发现。方法：利用MIMIC-IV数据库，采用极端梯度提升（XGBoost）分类器，基于心电图特征与人口学变量预测多种胸部放射学发现。针对每个目标独立进行递归特征消除以筛选关键预测因子。模型性能通过受试者工作特征曲线下面积（AUROC）评估，并采用自举法计算95%置信区间。应用Shapley加性解释（SHAP）分析特征贡献。结果：模型成功预测了多种胸部放射学发现，表现各异。特征选择使各预测任务针对性优化，加入人口学变量后性能持续提升。SHAP分析揭示心电图特征对放射学预测具有临床意义的贡献。结论：心电图特征结合人口学信息可作为部分胸部放射学发现的替代指标，在影像资源受限环境下实现早期分诊或预筛。可解释机器学习展现出支持放射科工作流、改善患者照护的潜力。
原文摘要 · Abstract (English)
Purpose: Chest X-rays are essential for diagnosing pulmonary conditions, but limited access in resource-constrained settings can delay timely diagnosis. Electrocardiograms (ECGs), in contrast, are widely available, non-invasive, and often acquired earlier in clinical workflows. This study aims to assess whether ECG features and patient demographics can predict chest radiograph findings using an interpretable machine learning approach. Methods: Using the MIMIC-IV database, Extreme Gradient Boosting (XGBoost) classifiers were trained to predict diverse chest radiograph findings from ECG-derived features and demographic variables. Recursive feature elimination was performed independently for each target to identify the most predictive features. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) with bootstrapped 95% confidence intervals. Shapley Additive Explanations (SHAP) were applied to interpret feature contributions. Results: Models successfully predicted multiple chest radiograph findings with varying accuracy. Feature selection tailored predictors to each target, and including demographic variables consistently improved performance. SHAP analysis revealed clinically meaningful contributions from ECG features to radiographic predictions. Conclusion: ECG-derived features combined with patient demographics can serve as a proxy for certain chest radiograph findings, enabling early triage or pre-screening in settings where radiographic imaging is limited. Interpretable machine learning demonstrates potential to support radiology workflows and improve patient care.
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