ArticleBMC medical informatics and decision making2010
A method for encoding clinical datasets with SNOMED CT.
Article in BMC medical informatics and decision making, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Diagnosis classification in EMR data using latent representations and SNOMED-CT mapping for improved medical data integration.Medical & biological engineering & computing · 2026Article
- Semantics in action: a guide for representing clinical data elements with SNOMED CT.Journal of biomedical semantics · 2025Article
- A SNOMED CT Mapping Guideline for the Local Terms Used to Document Clinical Findings and Procedures in Electronic Medical Records in South Korea: Methodological Study.JMIR medical informatics · 2023Article
- Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review.Journal of medical Internet research · 2021Article
- Clinical Term Normalization Using Learned Edit Patterns and Subconcept Matching: System Development and Evaluation.JMIR medical informatics · 2021Article
- Review
- Are Randomized Controlled Trials the (G)old Standard? From Clinical Intelligence to Prescriptive Analytics.Journal of medical Internet research · 2016Article
- Normalizing clinical terms using learned edit distance patterns.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Methods and applications for visualization of SNOMED CT concept sets.Applied clinical informatics · 2014Article
- A survey of SNOMED CT implementations.Journal of biomedical informatics · 2013Article
- Towards Converting Clinical Phrases into SNOMED CT Expressions.Biomedical informatics insights · 2013Article
- Measuring diversity in medical reports based on categorized attributes and international classification systems.BMC medical informatics and decision making · 2012Article
- Implementing SNOMED CT for Quality Reporting: Avoiding Pitfalls.Applied clinical informatics · 2011Article
- Clinical Clarity versus Terminological Order - The Readiness of SNOMED CT Concept Descriptors for Primary Care.MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOver the past decade there has been a growing body of literature on how the Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) can be implemented and used in different clinical settings. Yet, for those charged with incorporating SNOMED CT into their organisation's clinical applications and vocabulary systems, there are few detailed encoding instructions and examples available to show how this can be done and the issues involved. This paper describes a heuristic method that can be used to encode clinical terms in SNOMED CT and an illustration of how it was applied to encode an existing palliative care dataset.
methodsThe encoding process involves: identifying input data items; cleaning the data items; encoding the cleaned data items; and exporting the encoded terms as output term sets. Four outputs are produced: the SNOMED CT reference set; interface terminology set; SNOMED CT extension set and unencodeable term set.
resultsThe original palliative care database contained 211 data elements, 145 coded values and 37,248 free text values. We were able to encode ~84% of the terms, another ~8% require further encoding and verification while terms that had a frequency of fewer than five were not encoded (~7%).
conclusionsFrom the pilot, it would seem our SNOMED CT encoding method has the potential to become a general purpose terminology encoding approach that can be used in different clinical systems.
Indexed as
Identifiers
What OpenQuestion holds
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.