Improvement of covid-19 treatment with machine learning

Currently, testing the population more at risk to be severely ill is a cumbersome process. There is a limit to the capacity and the resources of the healthcare system that show a need to be more efficient in discovering infected person. Being able to quickly detect infected person helps reduce the risk of infecting others and it is especially important in environments like nursing homes where there is a high density of person at risk. Using an earpiece that work as a connected object, it is possible to monitor some signals such as cardiac rhythm, level of CO2, and cough. Using machine learning, those data could be used to detect automatically if a person has symptoms that should be checked by a health professional. It would improve the efficiency of detecting infected persons and, therefore, help healthcare providers manage the workload caused by covid-19.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

JACOBB

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program: