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
影响 Enhances trustworthiness of AI for critical healthcare applications by improving uncertainty quantification under real-world conditions.
排序理由 The cluster contains an academic paper detailing research findings on AI model performance.
- conformal prediction
- deep ensembles
- diastolic blood pressure
- Gaussian negative log-likelihood
- isotonic regression
- mean squared error
- Mohammad Moulaeifard
- Monte Carlo dropout
- PulseDB
- systolic blood pressure
- XResNet1D-50
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
