ArticleLearning health systems2022
Categorizing metadata to help mobilize computable biomedical knowledge.
Article in Learning health systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed.
- Article
- A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data.Journal of biomedical semantics · 2025Article
- Trends in electronic health record metadata use for management purposes.Learning health systems · 2025Article
- An automation framework for clinical codelist development validated with UK data from patients with multiple long-term conditions.BMC medical research methodology · 2025Article
- Ten simple rules to make computable knowledge shareable and reusable.PLoS computational biology · 2024Article
- Article
- Transforming health and well-being through publishing computable biomedical knowledge (CBK).Learning health systems · 2023Article
- Guiding principles for technical infrastructure to support computable biomedical knowledge.Learning health systems · 2023Article
- CBK model composition using paired web services and executable functions: A demonstration for individualizing preventive services.Learning health systems · 2023Article
- Characterizing variability of electronic health record-driven phenotype definitions.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Summary of fifth annual public MCBK meeting: Mobilizing computable biomedical knowledge (CBK) around the world.Learning health systems · 2023Article
- Summary of fourth annual MCBK public meeting: Mobilizing computable biomedical knowledge-metadata and trust.Learning health systems · 2022Article
- Categorizing metadata to help mobilize computable biomedical knowledge.Learning health systems · 2022Article
- Desiderata for the development of next-generation electronic health record phenotype libraries.GigaScience · 2021Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionComputable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine-interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T.
methodsWe examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject-predicate-object triples to more clearly differentiate metadata categories.
resultsWe defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK-to-CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories.
conclusionA wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.