Copertina

A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data

Salvato in:
Pubblicato in:Journal of Biomedical Semantics vol. 16 (2025), p. 1-13
Autore principale: Ansari, Adeel
Altri autori: Conte, Marisa, Flynn, Allen, Paturkar, Avanti
Pubblicazione:
Springer Nature B.V.
Soggetti:
Canada
Software
Computation
Metadata
Data processing
Linked Data
Datasets
Graphs
Body mass index
Ontology
Standards
Mental disorders
Enrichment
Biomedical research
Knowledge management
Semantic web
Knowledge representation
Electronic medical records
Electronic health records
Machine learning
Semantics
Pipelining (computers)
Information processing
Mental health
Resource Description Framework-RDF
Libraries
Accesso online:Citation/Abstract
Full Text
Full Text - PDF
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
Abstract: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.
ISSN:2041-1480
DOI:10.1186/s13326-025-00335-4
Fonte:Health & Medical Collection