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English(EN) Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography

AI模型在血压估算可靠性方面表现出改进

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员调查了深度学习模型在光电容积脉搏波描记法（PPG）信号血压估算中不确定性量化的可靠性。研究发现，与蒙特卡洛丢弃法（MCD）相比，深度集成（DE）在领域迁移下提供了更大的鲁棒性。重新校准的高斯负对数似然（GNLL）方法，特别是结合DE和共形预测或温度缩放，为收缩压和舒张压提供了最佳的不确定性校准。 AI

影响 通过在真实世界条件下改进不确定性量化，增强了AI在关键医疗保健应用中的可信度。

排序理由 该集群包含一篇详细介绍AI模型性能研究结果的学术论文。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AI模型在血压估算可靠性方面表现出改进

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Mohammad Moulaeifard, Ciaran Bench, Philip J. Aston, Nils Strodthoff ·

    域迁移下的不确定性可靠性：一项关于光电容积脉搏波图驱动的血压估计研究

    arXiv:2605.18008v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we assessed predictive performance and uncertainty rel…

  2. arXiv stat.ML TIER_1 English(EN) · Nils Strodthoff ·

    领域迁移下的不确定性可靠性：一项关于光电容积脉搏波描记法中数据驱动血压估算的研究

    Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we assessed predictive performance and uncertainty reliability for deep learning-based blood pressure (B…

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