Biomedical subjects

John S Carter

Publications and source records attributed to John S Carter.

4 recordsLinked to original sources

PubMed2004

Evaluation of SNOMED coverage of Veterans Health Administration terms.

Veterans Health Administration (VHA) is now evaluating use of SNOMED-CT. This paper reports the first phase of this evaluation, which examines the coverage of SNOMED-CT for problem list entries. Clinician expressions in VA problem lists are quite diverse compared to the content of the current VA terminology Lexicon. We selected a random set of 5054 narratives that were previously "unresolved" against the Lexicon. These narratives were mapped to SNOMED-CT using two automated tools. Experts reviewed a subset of the tools' matched, partly matched, and un-matched narratives. The automated tools produced exact or partial matches for over 90% of the 5054 unresolved narratives. SNOMED-CT has promise as a coding system for clinical problems. In subsequent studies, VA will examine the coverage of SNOMED for other clinical domains, such as drugs, allergies, and physician orders.

Forms and Records Control↗

PubMed2003

Adequacy of representation of the National Drug File Reference Terminology Physiologic Effects reference hierarchy for commonly prescribed medications.

The National Drug File Reference Terminology contains a novel reference hierarchy to describe physiologic effects (PE) of drugs. The PE reference hierarchy contains 1697 concepts arranged into two broad categories; organ specific and generalized systemic effects. This investigation evaluated the appropriateness of the PE concepts for classifying a random selection of commonly prescribed medications. Ten physician reviewers classified the physiologic effects of ten drugs and rated the accuracy of the selected term. Inter reviewer agreement, overall confidence, and concept frequencies were assessed and were correlated with the complexity of the drug's known physiologic effects. In general, agreement between reviewers was fair to moderate (kappa 0.08-0.49). The physiologic effects modeled became more disperse with drugs having and inducing multiple physiologic processes. Complete modeling of all physiologic effects was limited by reviewers focusing on different physiologic processes. The reviewers were generally comfortable with the accuracy of the concepts selected. Overall, the PE reference hierarchy was useful for physician reviewers classifying the physiologic effects of drugs. Ongoing evolution of the PE reference hierarchy as it evolves should take into account the experiences of our reviewers.

Drug Therapy↗

PubMed2002

Bethesda proposals for classification of nonlymphoid hematopoietic neoplasms in mice.

The hematopathology subcommittee of the Mouse Models of Human Cancers Consortium recognized the need for a classification of murine hematopoietic neoplasms that would allow investigators to diagnose lesions as well-defined entities according to accepted criteria. Pathologists and investigators worked cooperatively to develop proposals for the classification of lymphoid and nonlymphoid hematopoietic neoplasms. It is proposed here that nonlymphoid hematopoietic neoplasms of mice be classified in 4 broad categories: nonlymphoid leukemias, nonlymphoid hematopoietic sarcomas, myeloid dysplasias, and myeloid proliferations (nonreactive). Criteria for diagnosis and subclassification of these lesions include peripheral blood findings, cytologic features of hematopoietic tissues, histopathology, immunophenotyping, genetic features, and clinical course. Differences between murine and human lesions are reflected in the terminology and methods used for classification. This classification will be of particular value to investigators seeking to develop, use, and communicate about mouse models of human hematopoietic neoplasms.

Animals↗

PubMed2002

Initializing the VA medication reference terminology using UMLS metathesaurus co-occurrences.

We developed and evaluated a UMLS Metathesaurus Co-occurrence mining algorithm to connect medications and diseases they may treat. Based on 16 years of co-occurrence data, we created 977 candidate drug-disease pairs for a sample of 100 ingredients (50 commonly prescribed and 50 selected at random). Our evaluation showed that more than 80% of the candidate drug-disease pairs were rated "APPROPRIATE" by physician raters. Additionally, there was a highly significant correlation between the overall frequency of citation and the likelihood that the connection was rated "APPROPRIATE." The drug-disease pairs were used to initialize term definitions in an ongoing effort to build a medication reference terminology for the Veterans Health Administration. Co-occurrence mining is a valuable technique for initializing term definitions in a large-scale reference terminology creation project.

Algorithms↗