Physics-Informed Neural Networks for Modeling Physiological Time Series: A Case Study with Continuous Blood Pressure.

ABSTRACT: The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model the input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need to be trained with a significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PIN

SUBMITTER: Sel K 

PROVIDER: S-EPMC9882661 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature