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AI models show improved blood pressure estimation reliability

By PulseAugur Editorial · [2 sources] ·

Researchers investigated the reliability of uncertainty quantification in deep learning models for blood pressure estimation from photoplethysmography (PPG) signals. The study found that deep ensembles (DE) offer greater robustness under domain shift compared to Monte Carlo dropout (MCD). Recalibrated Gaussian negative log-likelihood (GNLL) methods, particularly with DE and conformal prediction or temperature scaling, provided the best uncertainty calibration for both systolic and diastolic blood pressure. AI

IMPACT Enhances trustworthiness of AI for critical healthcare applications by improving uncertainty quantification under real-world conditions.

RANK_REASON The cluster contains an academic paper detailing research findings on AI model performance.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI models show improved blood pressure estimation reliability

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The cluster contains an academic paper detailing research findings on AI model performance.
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COVERAGE [2]

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

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

    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 ·

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

    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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