用于稳健跨数据集临床预测的协调可解释心电图波形特征
Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction
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中文总结 AI 辅助
研究针对心电图模型跨医院迁移难的问题，构建协调可解释的心电图波形特征表示，训练XGBoost模型并进行多任务跨数据集泛化测试，验证两个假设，结果显示该特征接口能保持性能、支持外部验证，为临床预测提供新方案。
中文摘要 AI 辅助
心电图广泛用于心血管风险预测，但由于协议、人群和测量差异，模型往往无法在不同医院间迁移。我们使用两个大型队列（MIMIC-IV和艾伯塔队列）对心力衰竭分类、30天全因死亡率和窦性心律心电图30天死亡率这三个任务进行跨数据集泛化基准测试。为减少特定供应商的测量不匹配，我们构建了一种直接从原始波形计算的协调、可解释特征表示：FeatureDB形态/心率变异性摘要加上紧凑的时频描述符（自回归和小波特征）。我们在这个统一特征空间上训练XGBoost模型，并通过患者不相交的内部和双向外部测试进行评估。我们预先指定了两个假设：（H1）在迁移情况下，外部AUROC至少保留源站点内部AUROC的90%，（H2）协调特征集的内部AUROC保持在数据集原生机器测量模型的10%以内。在各个任务中，内部AUROC为0.79 - 0.82，跨数据集AUROC为0.74 - 0.78，迁移时AUPRC有更大且与方向相关的变化。作为探索性基准，直接在原始心电图波形上结合年龄和性别训练的端到端ConvNeXt模型实现了更高的内部AUROC，而协调表示在相对跨数据集迁移稳定性方面仍具有竞争力。这些发现表明，一致的波形衍生特征接口可保持性能，支持实际的外部验证，并为跨站点临床预测提供了一种透明的替代方案。
英文摘要
Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences. We benchmark cross-dataset generalization on three tasks - heart failure classification, 30-day all-cause mortality, and 30-day mortality among sinus-rhythm ECGs - using two large cohorts (MIMIC-IV and the Alberta Cohort). To reduce vendor-specific measurement mismatch, we build a harmonized, interpretable feature representation computed directly from raw waveforms: FeatureDB morphology/heart-rate-variability summaries plus compact time-frequency descriptors (autoregressive and wavelet features). We train XGBoost models on this unified feature space and evaluate with patient-disjoint internal and bidirectional external testing. We pre-specify two hypotheses: (H1) external AUROC retains at least 90% of source-site internal AUROC under transfer, and (H2) internal AUROC of the harmonized feature set stays within 10% of dataset-native machine-measurement models. Across tasks, internal AUROC is 0.79-0.82 and cross-dataset AUROC is 0.74-0.78, with larger and direction-dependent AUPRC shifts under transfer. As an exploratory benchmark, an end-to-end ConvNeXt model trained directly on raw ECG waveforms with age and sex achieves higher internal AUROC, while the harmonized representation remains competitive in relative cross-dataset transfer stability. These findings show that a consistent waveform-derived feature interface preserves performance, supports realistic external validation, and provides a transparent alternative for cross-site clinical prediction.
