Data-driven Automated Hypovigilance Detection using Physiological Parameters

In this research project, the intern will explore ways to detect hypovigilance with vital signs collected from the Intensive Care Unit (ICU). The intern will first study existing research and models to understand which features are important for detecting these conditions. Next, they will analyze vital sign data from ICU patients to identify the most informative features for detecting hypovigilance and delirium. Using this information, the intern will then apply statistical analysis and machine learning techniques to develop models that can accurately detect these conditions. Different combinations of input variables will be tested to find the most effective model. The performance of these models will be evaluated using various metrics, and they will be optimized for use with new patients. Finally, the intern will write a scientific paper to share the project’s findings. The partner organization will benefit from improved methods for detecting hypovigilance which is very important in the transportation sector.

Faculty Supervisor:

Patrick Archambault

Student:

Partner:

Thales Canada Inc

Discipline:

Computer science

Sector:

Manufacturing; Professional, scientific and technical services

University:

Université Laval

Program: