ArticleJMIR medical informatics2016
A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources.
Article in JMIR medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Assessment and treatment of metabolic acidosis in CKD: a registry-based study.Scientific reports · 2026Article
- Kidney outcomes of SGLT2 inhibitors among older patients with diabetic kidney disease in real-world clinical practice: the Japan Chronic Kidney Disease Database Ex.BMJ open diabetes research & care · 2024Article
- Evaluating the associations between compliance with CKD guideline component metrics and renal outcomes.Scientific reports · 2024Article
- Clinical Feature Vector Generation using Unsupervised Graph Representation Learning from Heterogeneous Medical Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Kidney Outcomes Associated With SGLT2 Inhibitors Versus Other Glucose-Lowering Drugs in Real-world Clinical Practice: The Japan Chronic Kidney Disease Database.Diabetes care · 2021Article
- Article
- Medical 3D printing: methods to standardize terminology and report trends.3D printing in medicine · 2017Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHealth level seven version 2.5 (HL7 v2.5) is a widespread messaging standard for information exchange between clinical information systems. By applying Semantic Web technologies for handling HL7 v2.5 messages, it is possible to integrate large-scale clinical data with life science knowledge resources.
objectiveShowing feasibility of a querying method over large-scale resource description framework (RDF)-ized HL7 v2.5 messages using publicly available drug databases.
methodsWe developed a method to convert HL7 v2.5 messages into the RDF. We also converted five kinds of drug databases into RDF and provided explicit links between the corresponding items among them. With those linked drug data, we then developed a method for query expansion to search the clinical data using semantic information on drug classes along with four types of temporal patterns. For evaluation purpose, medication orders and laboratory test results for a 3-year period at the University of Tokyo Hospital were used, and the query execution times were measured.
resultsApproximately 650 million RDF triples for medication orders and 790 million RDF triples for laboratory test results were converted. Taking three types of query in use cases for detecting adverse events of drugs as an example, we confirmed these queries were represented in SPARQL Protocol and RDF Query Language (SPARQL) using our methods and comparison with conventional query expressions were performed. The measurement results confirm that the query time is feasible and increases logarithmically or linearly with the amount of data and without diverging.
conclusionsThe proposed methods enabled query expressions that separate knowledge resources and clinical data, thereby suggesting the feasibility for improving the usability of clinical data by enhancing the knowledge resources. We also demonstrate that when HL7 v2.5 messages are automatically converted into RDF, searches are still possible through SPARQL without modifying the structure. As such, the proposed method benefits not only our hospitals, but also numerous hospitals that handle HL7 v2.5 messages. Our approach highlights a potential of large-scale data federation techniques to retrieve clinical information, which could be applied as applications of clinical intelligence to improve clinical practices, such as adverse drug event monitoring and cohort selection for a clinical study as well as discovering new knowledge from clinical information.
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