Aging Powerfully: AI Blueprint for Chronic Disease Management and Social Determinants of Longevity: 2025 National Inter-University Health Data and AI Inquiry Program | Indicium 2025 Conference Proceedings
Notice bibliographique
Résumé
The National Inter-University Health Data and AI Inquiry Program is a unique, interdisciplinary educational initiative that bridges the gap between traditional academic coursework and real-world data science research. Designed to foster agile learning and critical thinking, the program provides both undergraduate and graduate students with an exceptional opportunity to engage with Open Data and apply advanced data science techniques to meaningful, socially relevant health research challenges. Through this program, students develop and refine skills in uncovering hidden patterns and trends in both structured and unstructured datasets. They gain hands-on experience using a wide range of analytical tools and programming languages—such as Python, R, and diverse machine learning frameworks—to conduct in-depth data analysis. Indicium is an annual research mentorship program and competition designed to introduce undergraduate students to the process of independent scientific inquiry. Tailored for students with limited research experience, Indicium connects participants with mentors from diverse STEM backgrounds—including faculty members, graduate students, and medical professionals—who guide them through every stage of the research process. Throughout the program, students receive mentorship on research design, data analysis, and scientific communication, while also benefiting from skill-building workshops and networking opportunities with the broader scientific community. The theme for 2025, “Aging Powerfully: AI Blueprint for Chronic Disease Management and Social Determinants of Longevity,” challenged participants to explore how data and AI can advance our understanding of longevity and chronic disease management. Their investigations covered a wide array of topics—from designing AI-powered wearables for arthritis care, to leveraging sensor data for monitoring physical activity, to modelling gut-brain axis biomarkers in Alzheimer’s disease using machine learning. We are continually inspired by the intellectual curiosity, creativity, and analytical skills demonstrated by this next generation of researchers. Their work not only contributes to the scientific discourse around longevity and chronic disease management but also exemplifies the spirit of innovation that these programs seek to foster. To showcase and celebrate their achievements, two national conferences were held in July 2025: the Eastern Canada Conference at the University of Toronto (July 10, 2025) and the Western Canada Conference at the University of Calgary (July 18, 2025). On behalf of STEM Fellowship, we extend our heartfelt congratulations to all participants. We also thank our dedicated team of STEM Fellowship volunteers whose tireless support made these programs possible. We would also like to gratefully acknowledge the invaluable partnership and contributions of Research Canada, Canadian Science Publishing, CPHIN, Underline, and Overleaf. Finally, we are deeply grateful to the University of Calgary Institutes for Transdisciplinary Scholarship for their generous support in enabling the publication of these conference proceedings. In partnership with Canadian Personalized Healthcare Innovation Network, Research Canada, Roche, Canadian Science Publishing, Underline.io, Overleaf. This publication was made possible by the generous support of the University of Calgary Institutes for Transdisciplinary Scholarship.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».