View publication

This paper was accepted at the workshop Deep Generative Models for Health at NeurIPS 2023.

Cardiovascular diseases (CVDs) are a major global health concern, making the longitudinal monitoring of cardiovascular biomarkers vital for early diagnosis and intervention. A core challenge is the inference of cardiac pulse parameters from pulse waves, especially when acquired from wearable sensors at peripheral body locations. Traditional machine learning (ML) approaches face hurdles in this context due to the scarcity of labeled data, primarily sourced from clinical settings. Simultaneously, physical models, like the whole-body 1D hemodynamics simulators, although informative, struggle with the inverse problem and the complications posed by parameter interactions. Recent work has turned to simulation-based inference (SBI) to inform parameter inference by leveraging model simulations. Still, transferring predictors from simulations to real-world data remains a challenge due to model misspecifications. Addressing these issues, this paper presents a novel hybrid learning approach. By fusing a pulse-wave propagation simulator with a data-driven correction model, our methodology aims to blend the rigor of physical models with the flexibility of ML, offering a promising avenue for effective cardiovascular biomarker monitoring.

Related readings and updates.

Simulation-based Inference for Cardiovascular Models

This paper was accepted at the workshop Machine Learning and the Physical Sciences at NeurIPS 2023. Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. This comes naturally at the cost of increasing complexity since state-of-the-art models are non-linear partial differential equations depending on many parameters. While such tools are routinely used to…
See paper details

Apple Workshop on Machine Learning for Health 2023

Earlier this year, Apple hosted the Workshop on Machine Learning for Health. This two-day hybrid event brought together Apple and the academic research community and clinicians to discuss state-of-the-art machine learning (ML) research in health.

See event details