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. To establish a baseline, we created an ICD-9-CM terminology server and directly mapped textual descriptions of the VBA disability codes to ICD-9-CM. We next mapped ICD-9-CM and the VBA Disability codes to SNOMED CT. The SNOMED CT mappings were matched across classification systems and terms from related concepts were displayed for an expert coder's review. We report the rate of direct ICD-9-CM to VBA Disability Code mapping (26%), the eventual success of the SNOMED CT based crosswalk (95%) and the rate at which the reviewer had to add codes to complete the mapping (99%). The method using the SNOMED CT crosswalk provided significantly better coverage than the ICD-9-CM direct mapping alone (Pearson Chi Square test; p<0.001). We conclude that SNOMED CT can be a useful adjunct to direct mapping between administrative classifications.

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. From July 2003 through January 2004, 2 physician reviewers compared the Master Sheet Index text with the SNOMED CT terms that were automatically mapped by a vocabulary server or that they identified using a vocabulary browser and rated the "correctness" of the match. If the 2 reviewers disagreed, a third reviewer adjudicated. We evaluated the specificity, sensitivity, and positive predictive value of SNOMED CT. RESULTS: Of the 4996 problems in the test set, SNOMED CT correctly identified 4568 terms (true-positive results); 36 terms were true negatives, 9 terms were false positives, and 383 terms were false negatives. SNOMED CT had a sensitivity of 92.3%, a specificity of 80.0%, and a positive predictive value of 99.8%. CONCLUSION: SNOMED CT, when used as a compositional terminology, can exactly represent most (92.3%) of the terms used commonly in medical problem lists. Improvements to synonymy and adding missing modifiers would lead to greater coverage of common problem statements. Health care organizations should be encouraged and provided incentives to begin adopting SNOMED CT to drive their decision-support applications.

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.

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. We evaluated the ability of SNOMED CT to represent terms used for interface objects (e.g., labels and captions) and concepts used for data and branching logic in a general medical evaluation template in use within the Department of Veterans Affairs. METHODS: The general medical evaluation form definition, report definition, and script files were parsed and 1573 expressions were mapped into SNOMED CT. Compositional expressions required to represent 1171 concepts. Double independent reviews were conducted. Exact concept level matches were used to evaluate reference coverage. Exact term level matches were required for interface terms. Semantics were analyzed for a randomly selected subset of 20 terms. RESULTS: Sensitivity of SNOMED CT as a reference terminology was 63.8% , ranging from 29.3% for history items to 92.4% for exam items. SNOMED CT's sensitivity as an "interface terminology" was 55.0%. 80% of the necessary linking semantics for the subset were present. Subgroup statistics are presented. DISCUSSION: SNOMED CT is promising as a terminology for knowledge representation underlying a large general medical evaluation. Its performed less well as an interface terminology.

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. Missed Conceptual Representation, Inaccurate Conceptual Representation, Missed Negation, Inaccurate identification of Negation). METHODS: 41 Clinical Documents (Medical Evaluations; sometimes outside of Mayo these are referred to as History and Physical Examinations) were parsed using the Mayo Vocabulary Server Parsing Engine. SNOMED-C was used to provide concept coverage for the clinical concepts in the record. These records resulted in identification of Concepts and textual clues to Negation. These records were reviewed by an independent medical terminologist, and the results were tallied in a spreadsheet. Where questions on the review arose Internal Medicine Faculty were employed to make a final determination. RESULTS: SNOMED-CT was used to provide concept coverage of the 14,792 Concepts in 41 Health Records from John's Hopkins University. Of these, 1,823 Concepts were identified as negative by Human review. The sensitivity (Recall) of the assignment of negation was 97.2% (p < 0.001, Pearson Chi-Square test; when compared to a coin flip). The specificity of assignment of negation was 98.8%. The positive likelihood ratio of the negation was 81. The positive predictive value (Precision) was 91.2% CONCLUSION: Automated assignment of negation to concepts identified in health records based on review of the text is feasible and practical. Lexical assignment of negation is a good test of true Negativity as judged by the high sensitivity, specificity and positive likelihood ratio of the test. SNOMED-CT had overall coverage of 88.7% of the concepts being negated.