Precision Health and Artificial Intelligence
This theme is led by Dr. Seema Mital from SickKids, Toronto, ON, Canada, and brings together experts to support network projects in:
The use of next-generation phenomics, genomics, transcriptomics and metabolomics coupled with social, behavioral and lifestyle factors (exposomics) to inform public health strategies for patients with different forms of heart failure (HF);
The implementation of AI and digital solutions in the diagnosis and management of HF.
A network-wide precision Health approach
Next-generation "omics" are being used by the Pediatrics, Translational Science and RV/N2 teams to develop AI-based algorithms for early diagnosis and outcome prediction in systolic and diastolic HF, right ventricular failure, and congenital heart disease populations. The Indigenous Health, Pediatrics, and HF Care Access teams are testing precision health technologies, including AI-guided mobile echocardiography, smart wearables, virtual monitoring, and home-based point-of-care laboratory testing, for earlier HF diagnosis, closer follow-up, and improved care for patients in remote and Indigenous communities, and vulnerable populations such as children and the elderly. HOPE-HF uses VIRTUES, a direct-to-participant digital health platform, to collect free-living, real-world data from wearables, lab measurements, and questionnaires to develop personalized HF care strategies. To deliver precision health and AI solutions, each network project will include 1-2 members from the Precision theme. The theme will also mentor trainees and investigators at all levels to enable these insights to be incorporated across the network, and to develop the next generation of AI and precision health researchers.
This theme brings together experts from various centers in Canada, such as SickKids, the Montreal Heart Institute, Laval University, and McMaster University. All have significant expertise in precision health and AI, including next-generation "omics", precision medicine, digital health, wearables, pharmacogenetics, computer science, reinforcement learning, physiological modeling, genetic and molecular epidemiology, machine learning, imaging and biomarkers.
In order to support the network at its best, the Precision Health and AI theme identified several priorities to work on:
Facilitate access to data through a national study catalog, a data sharing and governance strategy, prospective incorporation of universal data sharing language in consents, and by engaging patient theme on feedback on data governance;
Facilitate access to AI expertise by assigning liaisons from the Precision Health and AI theme to network projects/teams;
Training and mentorship in AI and precision medicine.