Walentin Palanchuk · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28507 · open-access PDF
实验室测量对于临床决策至关重要,但通常需要侵入性血液采样。最近的研究表明,ECG记录可能包含超出心率的信息,并提供与实验室值相关的信息。本论文研究NT-proBNP预测从ECG记录和患者元数据中使用多式深度学习框架,同时考虑基于ECG的预测的生理可靠性。
Machine translation (Meta M2M100); the English original follows.
Laboratory measurements are essential for clinical decision making but typically require invasive blood sampling. Recent research suggests that ECG recordings may contain information beyond cardiac rhythm and provide information related to laboratory values. This thesis investigates NT-proBNP prediction from ECG recordings and patient metadata using a multimodal deep learning framework, while considering the physiological plausibility of ECG-based prediction. It also compares a 1-lead configuration using lead II with the corresponding 12-lead configuration. Additional experiments on glucose, h
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