Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks, Bench, Ciaran, Desai, Vivek, Moulaeifard, Mohammad, Strodthoff, Nils, Aston, Philip, Thompson, Andrew

Authors

Mohammad Moulaeifard (ORCID: https://orcid.org/0000-0002-6195-4436)
Philip J. Aston (ORCID: https://orcid.org/0000-0003-0877-0627)
Vivek Desai
Nils Strodthoff (ORCID: https://orcid.org/0000-0003-4447-0162)
Ciaran Bench
Andrew Thompson

Institutions

Carl von Ossietzky Universität Oldenburg (DE)
National Physical Laboratory (GB)

Publication Details

Journal: Machine Learning Health
Published: 2025-10-15
DOI: https://doi.org/10.1088/3049-477x/ae0b74
Citations: 1
Primary Topic: Non-Invasive Vital Sign Monitoring
Type: article
Field-Weighted Citation Impact: 0.67

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article

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks

Mohammad Moulaeifard, Philip J. Aston, Vivek Desai, Nils Strodthoff et al.

Oct 15, 2025

1 citations

Machine Learning Health

Non-Invasive Vital Sign Monitoring

0.67

article

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks

Mohammad Moulaeifard, Philip J. Aston, Vivek Desai, Nils Strodthoff, Ciaran Bench, Andrew Thompson

October 15, 2025article en

1 citations

Abstract

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks, Bench, Ciaran, Desai, Vivek, Moulaeifard, Mohammad, Strodthoff, Nils, Aston, Philip, Thompson, Andrew

Machine Learning HealthVol. 1(1)

Carl von Ossietzky Universität Oldenburg (DE), National Physical Laboratory (GB)

Openalex Percentile: Top 26%

Non-Invasive Vital Sign Monitoring

0.67

DOI Open Access PDF OpenAlex

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