Biosignal Transformers for Advanced Blood Pressure Waveform Analysis

This project aims to develop advanced machine learning models to analyze arterial blood pressure (ABP) waveforms
from patients in intensive care units (ICUs). By using a large dataset and cutting-edge techniques like transformer
architectures and contrastive learning, the goal is to create models that can accurately predict patient outcomes, such
as ICU mortality and hospital stay lengths. This research will improve our understanding of how ABP data can be
used to support clinical decision-making and enhance patient care. The project will also benefit participating
institutions by advancing the application of machine learning in healthcare, providing valuable insights for both
academic research and real-world clinical practice.

Faculty Supervisor:

Bryan Tripp

Student:

Partner:

National Technical University of Ukraine

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Artificial Intelligence; Technology

University:

University of Waterloo

Program: