A teaching proposal for a short course on biomedical data science
Bibliographic record
Abstract
As the availability of big biomedical data advances, there is a growing need of university students trained professionally on analyzing these data and correctly interpreting their results. We propose here a study plan for a master's degree course on biomedical data science, by describing our experience during the last academic year. In our university course, we explained how to find an open biomedical dataset, how to correctly clean it and how to prepare it for a computational statistics or machine learning phase. By doing so, we introduce common health data science terms and explained how to avoid common mistakes in the process. Moreover, we clarified how to perform an exploratory data analysis (EDA) and how to reasonably interpret its results. We also described how to properly execute a supervised or unsupervised machine learning analysis, and now to understand and interpret its outcomes. Eventually, we explained how to validate the findings obtained. We illustrated all these steps in the context of open science principles, by suggesting to the students to use only open source programming languages (R or Python in particular), open biomedical data (if available), and open access scientific articles (if possible). We believe our teaching proposal can be useful and of interest for anyone wanting to start to prepare a course on biomedical data science.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.083 | 0.062 |
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 source (direct Gemma or distilled Codex), 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".