Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals | Paper Citation Record

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This paper cites Acquiring wearable photoplethysmography data in daily life: The PPG diary pilot study.

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Acquiring wearable photoplethysmography data in daily life: The PPG diary pilot study

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals https://www.qumphy.ptb.de/publications

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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This paper cites Solosenko, A.; Marozas, V.

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Solosenko, A.; Marozas, V

Reference 6

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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This paper cites Petrenas, A.; Paliakaite, B.; Sornmo, L.; Marozas, V.

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Petrenas, A.; Paliakaite, B.; Sornmo, L.; Marozas, V

Reference 10

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Low-complexity detection of atrial fibrillation in continuous long-term monitoring

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Screening for atrial fibrillation: A call for evidence

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Oscillometric assessment of arterial stiffness in everyday clinical practice

Reference 14

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Biological versus chronological aging: JACC focus seminar

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Sleep disorders, medical conditions, and road accident risk

Reference 20

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals The economic burden of (obstructive) sleep apnea

Reference 21

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Clinician-focused overview and developments in polysomnography

Reference 22

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Clinical use of a home sleep apnea test: An American Academy of Sleep Medicine position statement

Reference 23

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This paper cites STOP-Bang and NoSAS questionnaires as a screening tool for OSA: Which one is the best choice? Rev.

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals STOP-Bang and NoSAS questionnaires as a screening tool for OSA: Which one is the best choice? Rev

Reference 24

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Screening commercial vehicle drivers for obstructive sleep apnea: Tools, barriers, and recommendations

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Respiratory rate and pattern

Reference 26

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Clinical antecedents to in- hospital cardiopulmonary arrest

Reference 28

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Physiological values and procedures in the 24 h before ICU admission from the ward

Reference 29

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

Reference 30

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals The recognition and early management of critical illness

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals ECG-derived respiratory frequency estimation

Reference 32

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Deep learning for ECG analysis: Benchmarks and insights from PTB-XL

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Imagenet classification with deep convolutional neural networks

Reference 38

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Minirocket: A very fast (almost) deterministic transform for time series classification

Reference 39

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Deep Residual Learning for Image Recognition

Reference 41

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals A Wavelet Tour of Signal Processing

Reference 42

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Beyond HRV: Attractor reconstruction using the entire cardiovascular waveform data for novel feature extraction

Reference 43

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals A novel method to quantify arterial pulse waveform morphology: Attractor reconstruction for physiologists and clinicians

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Symmetric Projection Attractor Reconstruction: Embedding in higher dimensions

Reference 45

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Assessing mental stress from the photoplethysmogram: A numerical study

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Atrial fibrillation

Reference 51

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Extraction of f waves

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Detection of atrial fibrillation using a wrist-worn device

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals A practical guide to wavelets for metrology

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Photoplethysmogram analysis and applications: An integrative review

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Feature dimensionality reduction: A review

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals PPFS: Predictive Permutation Feature Selection

Reference 59

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Regression shrinkage and selection via the lasso

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Autoencoder and its various variants

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This paper cites Charlton, P.H.

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Charlton, P.H

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

Reference 65

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals A unified approach to interpreting model predictions

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This paper cites Why should I trust you?: Explaining the predictions of any classifier.

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Why should I trust you?: Explaining the predictions of any classifier

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition

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This paper cites International Vocabulary of Metrology — Basic and general concepts and associated terms (VIM) (3rd edition), Joint Committee for Guides in Metrology, JCGM 200, 2012.

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals International Vocabulary of Metrology — Basic and general concepts and associated terms (VIM) (3rd edition), Joint Committee for Guides in Metrology, JCGM 200, 2012

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Evaluation of measurement data — Guide to the expression of uncertainty in measurement, Joint Committee for Guides in Metrology, JCGM 100, 2008

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This paper cites A metrological framework for uncertainty evaluation in machine learning classification models.

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals A metrological framework for uncertainty evaluation in machine learning classification models

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Approaches for the production of reference materials with qualitative properties

Reference 72

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This paper cites Towards trustworthy atrial fibrillation classification from wearables data: Quantifying model uncertainty.

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Towards trustworthy atrial fibrillation classification from wearables data: Quantifying model uncertainty

Reference 73

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 75

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Strictly proper scoring rules, prediction, and estimation

Reference 76

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Probabilistic forecasts, calibration and sharpness

Reference 77

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Dropout as a Bayesian approximation: Representing model uncertainty in deep learning

Reference 78

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Probabilistic Machine Learning: An Introduction

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Benchmarking uncertainty disentanglement: Specialized uncertainties for specialized tasks

Reference 80

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

Reference 81

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Accurate uncertainties for deep learning using calibrated regression

Reference 82

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Transforming classifier scores into accurate multiclass probability estimates

Reference 83

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Conformal prediction

Reference 85

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Conformalized quantile regression

Reference 86

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Least ambiguous set-valued classifers with bounded error levels

Reference 87

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Large -scale probabilistic predictors with and without guarantees of validity

Reference 88

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Properties of the ENCE and other MAD-based calibration metrics

Reference 89

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Measuring calibration in deep learning

Reference 90

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing

Reference 91

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals A unifying theory of distance from calibration

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Uncertainty calibration error: A new metric for multi-class classification, 2021

Reference 93

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Extending confidence calibration to generalised measures of variation

Reference 94

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Evaluating and calibrating uncertainty prediction in regression tasks

Reference 95

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals A prediction interval-based approach to determine optimal structures of neural network metamodels

Reference 96

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Unresolved cited work

Reference 98

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals PulseDB: A large, cleaned dataset based on MIMIC -III and VitalDB for benchmarking cuff -less blood pressure estimation methods

Reference 99

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Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals Zhou, B

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