ÉTS welcomes two research chairs specialized in artificial intelligence applied to health
$1.5 million in funding over three years from the FRQSThursday, May 27, 2021
Thanks to $1.5 million in funding over three years from the Fonds de recherche du Québec – Santé (FRSQ), the École de technologie supérieure (ÉTS) will host two new research chairs on artificial intelligence applied to the health field. Éric Granger, professor in the Department of Systems Engineering, and Rita Noumeir, professor in the Department of Electrical Engineering, will hold these two research chairs, which they will co-direct with their colleagues at the Université de Montréal and Concordia University.
This chair program, which draws on the expertise of the two co-chairs in complementary fields, aims to train qualified personnel who will be able to work in a field combining artificial intelligence and health.
“ÉTS is the only university to have been awarded two research chairs under this program, which initially planned to fund only one for all of Quebec. This double award demonstrates that our researchers have acquired a very specific expertise in data science. Their scientific contribution will undoubtedly strengthen the international influence of this strategic pole. ÉTS is also about engineering for a healthier future,” said François Gagnon, Director General of ÉTS.
Research chair on the development and validation of clinical decision support systems using artificial intelligence
The work, which will be co-directed by Professor Rita Noumeir of ÉTS and Professor Philippe Jouvet of the Université de Montréal and the CHU Sainte-Justine Research Centre, will help healthcare professionals and managers make decisions more quickly in a healthcare context.
As such, intensive care units are an ideal setting for personalized medicine research because they collect a large amount of patient data*, and this data is collected at a high frequency. In addition, this data can be linked to observations, notes and summaries of medical procedures that are recorded in the patient’s electronic record. In short, this data contains a large mass of integrated, amalgamated and analyzed information that could improve care through the development of algorithms and methods based on artificial intelligence.
On the other hand, the diversity of formats in which this data is presented—be it through laboratory tests, physiological signals, radiological images or medical notes—and the absence or imprecision of some other data requires the development of new methods of data processing to support decision-making in healthcare. The two co-chairs and their team will seek to address these issues. Ultimately, they plan to create a powerful algorithm that will not only allow for real-time assessment of patient status and distress, but also reduce ICU readmission rates and better manage patient flow between care units.
“The funding we received will be almost entirely in the form of scholarships for students who will be able to develop their expertise in the field of artificial intelligence applied to health,” said Rita Noumeir, professor-researcher at ÉTS.
* This data collection will be supervised by an ethics committee and patients will be asked to consent to its use for research purposes.
Research chair in artificial intelligence and digital health for health behaviour change
How can we help people follow a treatment plan or adopt healthier habits when they use an online health service without human intervention? This is the question that Éric Granger, professor of engineering at ÉTS, and Simon Bacon, professor of behavioural psychology at Concordia University and researcher at the CIUSSS du Nord-de-l'Île-de-Montréal Research Centre, will attempt to answer.
Studies have shown that a person’s ambivalence—the tug-of-war between their desire to change and their reasons for not doing so—has an impact on their ability to adopt healthier behaviours. Yet, non-verbal expressions provide subtle clues to a person’s ambivalence, and these are not taken into account during an online intervention because there is no human being to interpret them.
That could change thanks to the research of Professor Granger, a data scientist. The professor and his team plan to develop new AI technologies that can interpret the non-verbal language of users of these services. By detecting their ambivalence and even distress or motivation, the service would tailor its interventions to be personalized to the user’s emotional state.
Until then, a large amount of multimodal data extracted from videos must first be analyzed through. Using specialized deep learning models, it will be possible to accurately assign an emotional state to a combination of data from various sources (e.g., images and sounds) that include, for example, facial expression, voice intonation, gestures or posture. It will also be necessary to improve the performance of deep neural networks in expression recognition, because although they perform well in several types of applications, they tend to degrade due to the small amount of data and the diversity of sources.
The outcome of this research will lead to interventions that will have an impact on changing health behaviours, including physical inactivity and unhealthy diet, which account for up to 80% of the risk of chronic non-communicable diseases.
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