A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data
Guardat en:
| Publicat a: | Journal of Biomedical Semantics vol. 16 (2025), p. 1-13 |
|---|---|
| Autor principal: | |
| Altres autors: | , , |
| Publicat: |
Springer Nature B.V.
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3247146211 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2041-1480 | ||
| 024 | 7 | |a 10.1186/s13326-025-00335-4 |2 doi | |
| 035 | |a 3247146211 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 265152 |2 nlm | ||
| 100 | 1 | |a Ansari, Adeel | |
| 245 | 1 | |a A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundFor 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.ResultsWe 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.ConclusionsKnowledge 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. | |
| 651 | 4 | |a Canada | |
| 653 | |a Software | ||
| 653 | |a Computation | ||
| 653 | |a Metadata | ||
| 653 | |a Data processing | ||
| 653 | |a Linked Data | ||
| 653 | |a Datasets | ||
| 653 | |a Graphs | ||
| 653 | |a Body mass index | ||
| 653 | |a Ontology | ||
| 653 | |a Standards | ||
| 653 | |a Mental disorders | ||
| 653 | |a Enrichment | ||
| 653 | |a Biomedical research | ||
| 653 | |a Knowledge management | ||
| 653 | |a Semantic web | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Electronic medical records | ||
| 653 | |a Electronic health records | ||
| 653 | |a Machine learning | ||
| 653 | |a Semantics | ||
| 653 | |a Pipelining (computers) | ||
| 653 | |a Information processing | ||
| 653 | |a Mental health | ||
| 653 | |a Resource Description Framework-RDF | ||
| 653 | |a Libraries | ||
| 700 | 1 | |a Conte, Marisa | |
| 700 | 1 | |a Flynn, Allen | |
| 700 | 1 | |a Paturkar, Avanti | |
| 773 | 0 | |t Journal of Biomedical Semantics |g vol. 16 (2025), p. 1-13 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3247146211/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3247146211/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3247146211/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |