用心电图和临床数据预测实验室异常，提前发现健康风险。
Abnormality Prediction and Forecasting of Laboratory Values from Electrocardiogram Signals Using Multimodal Deep Learning
- 融合心电图与患者信息，用深度学习做多模态异常预测。
- 24项指标预测准确率超0.70，部分达0.90以上，如NTproBNP。
- 适合临床早筛场景，可减少侵入性检测，提升监测效率。
本研究探讨利用心电图（ECG）数据结合基本患者元数据来估算和监测实验室异常的可行性。基于MIMIC-IV数据集，训练多模态深度学习模型，输入包括心电波形、人口统计学、生物测量及生命体征。模型采用结构化状态空间分类器，对元数据进行后期融合。将任务设定为每项异常的个体二分类问题，并以AUROC评估性能。模型在24项实验室指标的异常预测中表现优异，AUROC均高于0.70，其中24项可用于异常预测与前瞻性预报；涵盖心脏、肾功能、血液、代谢、免疫及凝血等类别。尤其对NTproBNP（>353 pg/mL）的预测效果最佳，AUROC > 0.90；其他超过0.85的指标包括血红蛋白（>17.5 g/dL）、白蛋白（>5.2 g/dL）和红细胞压积（>51%）。结果表明，结合心电图与临床数据可实现对实验室异常的早期预警与预测，提供一种非侵入性、低成本的替代方案，支持早期干预与更高效的患者监护。
原文摘要 · Abstract (English)
This study investigates the feasibility of using electrocardiogram (ECG) data combined with basic patient metadata to estimate and monitor prompt laboratory abnormalities. We use the MIMIC-IV dataset to train multimodal deep learning models on ECG waveforms, demographics, biometrics, and vital signs. Our model is a structured state space classifier with late fusion for metadata. We frame the task as individual binary classifications per abnormality and evaluate performance using AUROC. The models achieve strong performance, with AUROCs above 0.70 for 24 lab values in abnormality prediction and up to 24 in abnormality forecasting, across cardiac, renal, hematological, metabolic, immunological, and coagulation categories. NTproBNP (>353 pg/mL) is best predicted (AUROC > 0.90). Other values with AUROC > 0.85 include Hemoglobin (>17.5 g/dL), Albumin (>5.2 g/dL), and Hematocrit (>51%). Our findings show ECG combined with clinical data enables prompt abnormality prediction and forecasting of lab abnormalities, offering a non-invasive, cost-effective alternative to traditional testing. This can support early intervention and enhanced patient monitoring. ECG and clinical data can help estimate and monitor abnormal lab values, potentially improving care while reducing reliance on invasive and costly procedures.
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