A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data
Bibliographic record
Abstract
BACKGROUND: For clinical care and research, knowledge graphs with patient data can be enriched by extracting parameters from a knowledge graph and then using them as inputs to compute new patient features with pure functions. Systematic and transparent methods for enriching knowledge graphs with newly computed patient features are of interest. When enriching the patient data in knowledge graphs this way, existing ontologies and well-known data resource standards can help promote semantic interoperability. RESULTS: We developed and tested a new data processing pipeline for extracting, computing, and returning newly computed results to a large knowledge graph populated with electronic health record and patient survey data. We show that RDF data resource types already specified by Health Level 7's FHIR RDF effort can be programmatically validated and then used by this new data processing pipeline to represent newly derived patient-level features. CONCLUSIONS: Knowledge graph technology can be augmented with standards-based semantic data processing pipelines for deploying and tracing the use of pure functions to derive new patient-level features from existing data. Semantic data processing pipelines enable research enterprises to report on new patient-level computations of interest with linked metadata that details the origin and background of every new computation.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".