Grouping in AI
acute care for the child

Multimodal database for critically ill children

Intensive care units (ICUs) care for patients in life-threatening distress, but face three main obstacles to overcome: (1) delays in detecting and managing life-threatening distress, (2) delays in implementing best practices, (3) lack of research tools to conduct pragmatic clinical trials integrated into ICUs. This requires access to all clinical data in digital format, including therapeutic actions and videos of critically ill patients.

To address this issue, we are developing a multimodal infrastructure for storing and analyzing intensive care patient data (electronic medical record data, video surveillance with standard (red-green-blue), infrared and depth (3D) images, physiological signals (heart rate, pressures, etc.), laboratory data and imaging (including electrical impedance tomography), therapeutic data from infusion pumps, ventilators and other media)..

This infrastructure, funded by the Canada Foundation for Innovation, the Quebec Ministry of Health, the Université de Montréal and CHU Ste-Justine, is the cornerstone of the research conducted by our group.