175 citations · 645 across the 17 of their papers we have counts for
17 papers
Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters
Emanuele Palumbo, Sorawit Saengkyongam, Maria R. Cervera +5
Continuous cardiovascular monitoring can play a key role in precision health. However, some fundamental cardiac biomarkers of interest, including stroke volume and cardiac output,…
Inferring Optical Tissue Properties from Photoplethysmography using Hybrid Amortized Inference
Jens Behrmann, Maria R. Cervera, Antoine Wehenkel +8
Smart wearables enable continuous tracking of established biomarkers such as heart rate, heart rate variability, and blood oxygen saturation via photoplethysmography (PPG). Beyond…
Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers
Laura Manduchi, Antoine Wehenkel, Jens Behrmann +6
Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular syst…
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Antoine Wehenkel, Juan L. Gamella, Ozan Sener +4
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However,…
Simulation-based Inference for Cardiovascular Models
Antoine Wehenkel, Laura Manduchi, Jens Behrmann +6
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. While such tools are routinely u…
Robust Hybrid Learning With Expert Augmentation
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu +3
Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data. Similarly to many ML algorithms, hybrid mo…