MétaCan
Menu
Back to cohort
Record W4392068448 · doi:10.1016/j.jbi.2024.104614

Improving the interoperability of drugs terminologies: Infusing local standardization with an international perspective

2024· article· en· W4392068448 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Biomedical Informatics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
FundersInstitut de Valorisation des DonnéesCanada First Research Excellence Fund
KeywordsSNOMED CTInteroperabilityComputer scienceStandardizationOntologySemantic interoperabilityService (business)Code (set theory)TerminologySystematized Nomenclature of MedicineInformation retrievalWorld Wide WebSoftware engineeringProgramming languageBusinessLinguistics

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this study is to describe how OCRx (Canadian Drug Ontology) has been built to address the dual need for local drug information integration in Canada and alignment with international standards requirements. METHODS: This paper delves into (i) the implementation efforts to meet the Identification of Medicinal Product (IDMP) requirements in OCRx, alongside the ontology update strategy, (ii) the structure of the ontology itself, (iii) the alignment approach with several reference Knowledge Organization Systems, including SNOMED CT, RxNorm, and the list of "Code Identifiant de Spécialité" (CIS-Code), and (iv) the look-up services developed to facilitate its access and utilization. RESULTS: Each OCRx release contains two distinct versions: the full and the up-to-date version. The full version encompasses all drugs with a DIN code sanctioned by Health Canada, while the up-to-date version is limited to drugs currently marketed in Canada. In the last release of OCRx, the full version comprises 162,400 classes; meanwhile, the up-to-date version consists of 36,909 classes. In terms of mappings with OCRx, substances in RxNorm and SNOMED CT fall below 40%, registering at 37% and 22% respectively. Meanwhile, mappings for CIS-Code achieve coverage of 61%. The strength mappings are notably low for RxNorm at 40% and for CIS-code at 28%. This affects the mapping of clinical drugs, which are predominantly alignable through post-coordinated expressions: 56% for RxNorm, 80% for SNOMED CT, and 35% for CIS-Code. The main support service of OCRx is a look-up service known as PaperRx that displays OCRx's entities based on description logic queries (DL-queries) performed through the classified structure of OCRx. The look-up services also contain a SPARQL endpoint, an OCRx OWL file downloader, and a RESTful API. DISCUSSION: The OCRx ontology demonstrates a significant effort towards integrating Canadian drug information with international standards. However, there are areas for improvement. In the future, our focus will be on refining the structure of OCRx for better classification capability and improvement of dosage conversion. Additionally, we aim to harness OCRx in constructing an ontology-based annotator, setting our sights on its deployment in real-world data integration scenarios.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.294
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it