Leveraging open data analytics and machine learning to improve diagnosis of diseases, patients’ care, and support: Proceedings from the 2023 Inter-University Big Data and AI Challenge
Bibliographic record
Abstract
STEM Fellowship’s Inter-University Big Data Challenge offers a distinctive opportunity for university students globally to engage in a hands-on learning experience that combines computational thinking and Big Data exploration to seek solutions to health-related challenges at the national, regional, community, and individual levels. It serves as an innovative platform for identifying and nurturing research and development talent through the application of computational science and effective scholarly communication. Within this program, participants gain access to a diverse range of workshops focused on data analytics, programming, and science communication. Through these workshops, students acquire valuable skills in Python, R, machine learning, LaTeX, and Overleaf, enabling them to tackle complex data-driven problems. By providing these tools and fostering experiential learning, the program equips students with the necessary knowledge and expertise to contribute meaningfully to the field of Data Science and its applications in various domains, including healthcare. This year, the program participants explored the theme of “Leveraging Open Data Analytics and Machine Learning to Improve Diagnosis of Diseases, Patients’ Care and Support” and suggested a whole spectrum of original Open Data and Machine Learning based ideas and solutions. The research topics presented encompass a wide range of areas, spanning from repurposing drugs for the treatment of rare diseases and employing machine learning techniques to detect the progression of Parkinson’s disease, to developing an ESG-focused governance framework aimed at enhancing patient care. Overall, we received submissions from student teams from practically all leading Canadian universities, mixed teams of students from Canada and the US, and Asian universities. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the program and wish them the best for their future academic and professional endeavors. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without generous support of our sponsors: Canadian Science Publishing, IntechOpen, JMIR Publications and adMare Community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".