FAIR-ification of structured Head and Neck Cancer clinical data for multi-institutional collaboration and federated learning
Bibliographic record
Abstract
Abstract Federated learning has been demonstrated as an acceptable clinical research methodology for producing analyses and models on dispersed datasets, without the need for exchanging individual patient-level data. Attention needs to be given to making repositories of clinical data Findable, Accessible, Interoperable and Reusable (FAIR) in order to realize the potential of such clinical data in federated learning applications. This work draws attention to FAIR-ification structured clinical data of Head and Neck cancer patients, generated in different parts of the world with incompatible terminologies. We began with an “open world” approach by converting the native datasets into the Resource Descriptor Framework format, and then applying a customized local annotation for each dataset to map the data fields to open access ontologies. This approach allows interactive data exploration by means of a federated SPARQL query-based dashboard. The annotations and dashboard visualizations were constructed without using the individual patient-level data. It is feasible to develop and validate multi-institutional statistical models with federated learning on top of the annotations that make the data FAIR. Findings are robust and potentially scalable to a larger number of participating institutions. The annotation methodology proposed here supports multiple simultaneous mappings (such as the data being re-used in multiple different projects) while keeping the native data the same. Future work may be to include certain rules and requirements for classes and predicates, and using the Shapes Constraint Language for checking the validity of the data.
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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 | 0.055 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".