Semantic interoperability on blockchain by generating smart contracts based on knowledge graphs
Bibliographic record
Abstract
Health 3.0 enables decision-making to be based on longitudinal data from multiple institutions spanning the patient's healthcare journey. Blockchain smart contracts can act as neutral and trustworthy intermediaries to implement such decision-making. In this distributed healthcare setting, transmitted data are structured using standards, such as Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), for semantic interoperability. Hence, the smart contract will require interoperability with the domain standard. However, it will also have to implement a complex communication setup to work in a distributed environment (e.g., using oracles), and be developed using special-purpose blockchain languages (e.g., Solidity). To support these requirements, we propose the encoding of smart contract logic using a high-level semantic Knowledge Graph (KG), which uses concepts and relations from a domain standard and additionally lists distributed data requirements. We subsequently deploy this semantic KG on blockchain via a hybrid on-/off-chain code-generation approach. We applied our approach to generate smart contracts for three health insurance cases from Medicare. We evaluated the generated contracts in terms of correctness and execution cost (i.e., gas) on blockchain. Finally, we discuss the suitability of blockchain—and by extension, our approach—for a number of healthcare use cases.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".