Archive for the ‘SNOMED’ Category

New SNOMED CT Data Files Available

Sunday, November 16th, 2014

New SNOMED CT Data Files Available

From the post:

NLM is pleased to announce the following releases available for download:

  1. A new subset from Convergent Medical Terminology (CMT) is now available for download from the UMLS Terminology Services (UTS) by UMLS licensees. This problem list subset includes concepts that KP uses within the ED Problem List. There are 2189 concepts in this file. SNOMED Concepts are based on the 1/31/2012 version of the International Release.

    For more information about CMT, please see the NLM CMT Frequently Asked Questions page.

  2. The Spanish Edition of the SNOMED CT International Release is now available for download.
  3. On behalf of the International Health Terminology Standards Development Organisation (IHTSDO), NLM is pleased to announce the release of the SNOMED CT International General/Family Practice subset (GP/FP Subset) and map from the GP/FP Subset to the International Classification of Primary Care (ICPC-2). This is the baseline work release resulting from the harmonization agreement between the IHTSDO and WONCA.

    The purpose of this subset is to provide the frequently used SNOMED CT concepts for use in general/family practice electronic health records within the following data fields: reason for encounter, and health issue. The purpose of the map from the SNOMED CT GP/FP subset to ICPC-2 is to allow for the granular concepts to be recorded by GPs/FPs at the point of care using SNOMED CT, with subsequent analysis and reporting using the internationally recognized ICPC-2 classification. However please note that use within clinical systems cannot be supported at this time. This Candidate Baseline is distributed for evaluation purposes only and should not be used in production clinical systems or in clinical settings.

    The subsets are aligned to the July 2014 SNOMED CT International Release. The SNOMED CT to ICPC-2 map is a Candidate Baseline, which IHTSDO expects to confirm as the Baseline release following the January 2015 SNOMED CT International Release.

If your work in any way touches upon medical teminology, Convergent Medical Terminology (CMT) and SNOMED CT (Systematized Nomenclature of Medicine–Clinical Terms), among other collections of medical terminology will be of interest to you.

Medical terminology is a small part of the world at large and you can see what it takes for the NLM to maintain a semblance of chaotic order. Great benefit flow even from a semblance of order but those benefits are not free.

A Tale of Five Languages

Monday, February 11th, 2013

Evaluating standard terminologies for encoding allergy information by Foster R Goss, Li Zhou, Joseph M Plasek, Carol Broverman, George Robinson, Blackford Middleton, Roberto A Rocha. (J Am Med Inform Assoc doi:10.1136/amiajnl-2012-000816)


Objective Allergy documentation and exchange are vital to ensuring patient safety. This study aims to analyze and compare various existing standard terminologies for representing allergy information.

Methods Five terminologies were identified, including the Systemized Nomenclature of Medical Clinical Terms (SNOMED CT), National Drug File–Reference Terminology (NDF-RT), Medication Dictionary for Regulatory Activities (MedDRA), Unique Ingredient Identifier (UNII), and RxNorm. A qualitative analysis was conducted to compare desirable characteristics of each terminology, including content coverage, concept orientation, formal definitions, multiple granularities, vocabulary structure, subset capability, and maintainability. A quantitative analysis was also performed to compare the content coverage of each terminology for (1) common food, drug, and environmental allergens and (2) descriptive concepts for common drug allergies, adverse reactions (AR), and no known allergies.

Results Our qualitative results show that SNOMED CT fulfilled the greatest number of desirable characteristics, followed by NDF-RT, RxNorm, UNII, and MedDRA. Our quantitative results demonstrate that RxNorm had the highest concept coverage for representing drug allergens, followed by UNII, SNOMED CT, NDF-RT, and MedDRA. For food and environmental allergens, UNII demonstrated the highest concept coverage, followed by SNOMED CT. For representing descriptive allergy concepts and adverse reactions, SNOMED CT and NDF-RT showed the highest coverage. Only SNOMED CT was capable of representing unique concepts for encoding no known allergies.

Conclusions The proper terminology for encoding a patient’s allergy is complex, as multiple elements need to be captured to form a fully structured clinical finding. Our results suggest that while gaps still exist, a combination of SNOMED CT and RxNorm can satisfy most criteria for encoding common allergies and provide sufficient content coverage.

Interesting article but some things that may not be apparent to the casual reader:


The Medical Dictionary for Regulatory Activities (MedDRA) was developed by the International Conference on Harmonisation (ICH) and is owned by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA) acting as trustee for the ICH steering committee. The Maintenance and Support Services Organization (MSSO) serves as the repository, maintainer, and distributor of MedDRA as well as the source for the most up-to-date information regarding MedDRA and its application within the biopharmaceutical industry and regulators. (source:

MedDRA has a metathesaurus with translations into: Czech, Dutch, French, German, Hungarian, Italian, Japanese, Portuguese, and Spanish.

Unique Ingredient Identifier (UNII)

The overall purpose of the joint FDA/USP Substance Registration System (SRS) is to support health information technology initiatives by generating unique ingredient identifiers (UNIIs) for substances in drugs, biologics, foods, and devices. The UNII is a non- proprietary, free, unique, unambiguous, non semantic, alphanumeric identifier based on a substance’s molecular structure and/or descriptive information.

The UNII may be found in:

  • NLM’s Unified Medical Language System (UMLS)
  • National Cancer Institutes Enterprise Vocabulary Service
  • USP Dictionary of USAN and International Drug Names (future)
  • FDA Data Standards Council website
  • VA National Drug File Reference Terminology (NDF-RT)
  • FDA Inactive Ingredient Query Application


National Drug File – Reference Terminology (NDF-RT)

The National Drug File – Reference Terminology (NDF-RT) is produced by the U.S. Department of Veterans Affairs, Veterans Health Administration (VHA).

NDF-RT combines the NDF hierarchical drug classification with a multi-category reference model. The categories are:

  1. Cellular or Molecular Interactions [MoA]
  2. Chemical Ingredients [Chemical/Ingredient]
  3. Clinical Kinetics [PK]
  4. Diseases, Manifestations or Physiologic States [Disease/Finding]
  5. Dose Forms [Dose Form]
  6. Pharmaceutical Preparations
  7. Physiological Effects [PE]
  8. Therapeutic Categories [TC]
  9. VA Drug Interactions [VA Drug Interaction]


MedDRA, UNII, and NDF-RT have been in use for years, MedDRA internationally in multiple languages. An uncounted number of medical records, histories and no doubt publications rely upon these vocabularies.

Assume the conclusion: SNOMED CT with RxNorm (links between drug vocabularies) provide the best coverage for “encoding common allergies.”

A critical question remains:

How to access medical records using other terminologies?

Recalling from the adventures of owl:sameAs (The Semantic Web Is Failing — But Why? (Part 5)) that any single string identifier is subject to multiple interpretations. Interpretations that can only be disambiguated by additional information.

You might present a search engine with string to string mappings but those are inherently less robust and harder to maintain than richer mappings.

The sort of richer mappings that are supported by topic maps.

Automated extraction of domain-specific clinical ontologies – Weds Oct. 5th

Monday, October 3rd, 2011

Automated extraction of domain-specific clinical ontologies by Chimezie Ogbuji from Case Western Research University School of Medicine. 10 AM PT Weds Oct. 5, 2011.

Full NCBO Webinar schedule:


A significant set of challenges in the use of large, source ontologies in the medical domain include: automated translation, customization of source ontologies, and performance issues associated with the use of logical reasoning systems to interpret the meaning of a domain captured in a formal knowledge representation.

SNOMED-CT and FMA are two reference ontologies that cover much of the domain of clinical medicine and motivate a better means for the re-use of such ontologies. In this presentation, the author will present a set of automated methods (and tools) for segmenting, merging, and surveying modules extracted from these ontologies for a specific domain.

I’m interested generally but in particular about the merging aspects, for obvious reasons. Another reason to be interested is some research I encountered recently on “outliers” in reasoning systems. Apparently there is a class of reasoning systems that simply “fall over” if they encounter a concept they recognize (or “think” they do) only to find it has some property (what makes it an “outlier”) that they don’t. Seems rather fragile to me but I haven’t finished running it to ground. Curious how these methods and tools handle the “outlier” issue.


Chimezie is a senior research associate in the Clinical Investigations Department of the Case Western Research University School of Medicine where he is responsible for managing, developing, and implementing Clinical and Translational Science Collaborative (CTSC) projects as well as clinical, biomedical, and administrative informatics projects for the Case Comprehensive Cancer Center.

His research interests are in applied ontology, knowledge representation, content repository infrastructure, and medical informatics. He has a BS in computer engineering from the University of Illinois and is a part-time PhD student in the Case Western School of Engineering. He most recently appeared as a guest editor in IEEE Internet Computing’s special issue on Personal Health Records in the August 2011 edition.


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The Neighborhood Auditing Tool – Update

Tuesday, October 26th, 2010

The Neighborhood Auditing Tool for the UMLS and its Source Terminologies is a presentation mentioned here several days ago.

If you missed it, go to: for the slides and WEBEX recording.

Pay close attention to:

The clear emphasis on getting user feedback during the design of the auditing interface.

The “neighborhood” concept he introduces has direct application to XML editing.

Find the “right” way to present parent/child/sibling controls to users and you would have a killer XML application.


  1. Slides 8 – 9. Other than saying this is an error (true enough), on what basis is that judgment made?
  2. Slides 18 – 20. Read the references (slide 20) on neighborhoods. Pick another domain, what aspects of neighborhoods are relevant? (3-5 pages, with citations)
  3. Slides 21 – 22. How do your neighborhood graphs compare to those here?
  4. Slides 23 – 46. Short summary of the features of NAT and no citation evaluation. Or, use NAT as basis for development of interface for another domain. (project)
  5. Slides 49 – 55. Visualizations for use and checking. Compare to current literature on visualization of vocabularies/ontologies. (project)
  6. Slides 56 – 58. Snomed browsing. Report on current status. (3-5 pages, citations)
  7. Slices 57 – 73. Work on neighborhoods and extents. To what extent is a “small intersection type” a sub-graph and research on sub-graphs applicable? Any number of issues and questions can be gleaned from this section. (project)