Using SNOMED CT as a reference terminology to cross map two highly pre-coordinated classification systems.

We hypothesized that SNOMED CT, a granular formal reference terminology, could be used to assist in the creation of a valid crosswalk between two administrative classifications: the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and the U.S. Veterans Benefits Administration (VBA) disability code set.

SNOMED CT: utility for a general medical evaluation template.

Background: The U.S. government has licensed SNOMED CT to permit broad-based evaluation and use of the terminology.

Evaluation of the content coverage of SNOMED CT: ability of SNOMED clinical terms to represent clinical problem lists.

Objective: To evaluate the ability of SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) version 1.0 to represent the most common problems seen at the Mayo Clinic in Rochester, Minn.

Material And Methods: We selected the 4996 most common nonduplicated text strings from the Mayo Master Sheet Index that describe patient problems associated with inpatient and outpatient episodes of care.

Standardization of microarray and pharmacogenomics data.

This chapter provides a bottom-up perspective on bioinformatics data standards, beginning with a historical perspective on biochemical nomenclature standards. Various file format standards were soon developed to convey increasingly complex and voluminous data that nomenclature alone could not effectively organize without additional structure and annotation. As areas of biochemistry and molecular biology have become more integral to the practice of modern medicine, broader data representation models have been created, from corepresentation of genomic and clinical data as a framework for drug research and discovery to the modeling of genotyping and pharmacogenomic therapy within the broader process of the delivery of health care.

A controlled trial of automated classification of negation from clinical notes.

Background: Identification of negation in electronic health records is essential if we are to understand the computable meaning of the records: Our objective is to compare the accuracy of an automated mechanism for assignment of Negation to clinical concepts within a compositional expression with Human Assigned Negation. Also to perform a failure analysis to identify the causes of poorly identified negation (i.e.

VA National Drug File Reference Terminology: a cross-institutional content coverage study.

Background: Content coverage studies provide valuable information to potential users of terminologies. We detail the VA National Drug File Reference Terminology's (NDF-RT) ability to represent dictated medication list phrases from the Mayo Clinic. NDF-RT is a description logic-based resource created to support clinical operations at one of the largest healthcare providers in the US.