Leveraging open data analytics and machine learning to improve mental health research and innovation: 2024 Inter-university big data challenge proceedingsIn partnership with Canadian Science Publishing, the Canadian Personalized Healthcare Innovation Network, and Underline.io
Notice bibliographique
Résumé
The National Inter-University Big Data and AI Challenge is an interdisciplinary, agile educational environment that bridges the gap between traditional coursework and real-world data science applications. The challenge provides undergraduate and graduate researchers with the unique opportunity to explore the intersection of Open Data and applied scientific research by developing and presenting their research amongst peers and professionals from academia and industry. By participating, students gain skills in uncovering hidden patterns and trends in structured and unstructured data using a wide range of data analytics tools and programming languages including Python, R, and various machine learning frameworks. They also gain extensive experience with scientific communication by compiling abstracts, manuscripts, posters, and videos which ultimately culminate in a presentation on Big Data Day. The guiding theme for the 2024 National Inter-University Big Data and AI Challenge was “Leveraging Open Data Analytics and Machine Learning to Improve Mental Health Research and Innovation”. During the 2024 event, we saw over 134 participants from over 15 different institutions and 15 different degree programs learn and utilize important techniques in analytics, artificial intelligence (AI), and machine learning (ML) in order to delve into the complexities of mental health diseases and treatments. These future leaders were tasked with using Open Data to enhance our understanding of mental health and explore areas for innovation regarding mental health practice and research, gaining critical insights into this field. Various topics were investigated, ranging from the socioeconomic determinants of mental health to differential outcomes in treatment efficacy across geographic boundaries. By applying computational thinking, students explored the interplay between mental health and external factors, ultimately contributing to the development of tailored interventions and personalized care plans. We are privileged to witness the analytical capabilities of this talented generation of students, and we are confident they will demonstrate excellence throughout their academic and professional careers. The 2024 Big Data Day, the culmination of our participants’ trailblazing research, was held at the Microsoft Headquarters in Toronto. On behalf of STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge and wish them success in their future endeavours. We also want to express our appreciation to all the STEM Fellowship volunteers who support this program. We greatly appreciate the valuable collaboration and support of our partners: Research Canada, Canadian Science Publishing, CPHIN, Underline, JMIR Publications, Overleaf.
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 | 0,019 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».