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Biomedical subjects

Mark S Tuttle

Publications and source records attributed to Mark S Tuttle.

7 recordsLinked to original sources
PubMed2004

NCI Thesaurus: using science-based terminology to integrate cancer research results.

Cancer researchers need to be able to organize and report their results in a way that others can find, build upon, and relate to the specific clinical conditions of individual patients. NCI Thesaurus is a description logic terminology based on current science that helps individuals and software applications connect and organize the results of cancer research, e.g., by disease and underlying biology. Currently containing some 34,000 concepts--covering chemicals, drugs and other therapies, diseases, genes and gene products, anatomy, organisms, animal models, techniques, biologic processes, and administrative categories--NCI Thesaurus serves applications and the Web from a terminology server. As a scalable, formal terminology, the deployed Thesaurus, and associated applications and interfaces, are a model for some of the standards required for the NHII (National Health Information Infrastructure) and the Semantic Web.

Computer Systems↗
PubMed2004

Achieving "source transparency" in the UMLS Metathesaurus.

The UMLS Metathesaurus is a syntactically uniform, concept-based, semantically enhanced representation of many of the world's authoritative biomedical vocabularies. Released several times a year, the Metathesaurus is becoming a common, longitudinally maintained source of the current versions of these vocabularies. As vocabularies become standards for reimbursement, reporting, interoperation, and use by applications, the vocabulary obtained from the Metathesaurus must be consistent with that obtainable from each vocabulary's authority. Effective with the first 2004 release, the Metathesaurus represents new and updated sources "transparently"--both users and applications are able to "see" each vocabulary in the Metathesaurus without any of the small losses of information introduced by abstractions used in previous versions. Thus, the Metathesaurus can continue to provide its many semantic and lexical value-added features while guaranteeing that original sources will be "visible" in intact form. Vocabulary users and application developers will benefit from the enhancements and economies of scale offered by the Metathesaurus, while preserving distinctions between content provided by external authorities and content added as part of the Metathesaurus development and maintenance process.

Unified Medical Language System↗
PubMed2004

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. METHODS: Medication list phrases were extracted from dictated patient notes from the Mayo Clinic. Algorithmic mappings to NDF-RT using the SmartAccess Vocabulary Server (SAVS) were presented to two non-VA physicians. The physicians used a terminology browser to determine the accuracy of the algorithmic mapping and the content coverage of NDF-RT. RESULTS: The 509 extracted documents on 300 patients contained 847 medication concepts in medication lists. NDF-RT covered 97.8% of concepts. Of the 18 phrases that NDF-RT did not represent, 10 were for OTC's and food supplements, 5 were for prescription medications, and 3 were missing synonyms. The SAVS engine properly mapped 773 of 810 phrases with an overall sensitivity (precision) was 95.4% and positive predictive value (recall) of 99.9%. CONCLUSIONS: This study demonstrates that NDF-RT has more general utility than its initial design parameters dictated

Abstracting and Indexing↗
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↗
PubMed2002

A semantic normal form for clinical drugs in the UMLS: early experiences with the VANDF.

A semantic normal form (SNF) for a clinical drug, designed to represent the meaning of an expression typically seen in a practitioner's medication order, has been developed and is being created in the UMLS Metathesaurus. The long term goal is to establish a relationship for every concept in the Metathesaurus with semantic type "clinical drug" with one or more of these semantic normal forms. First steps have been taken using the Veterans Administration National Drug File (VANDF). 70% of the entries in the VANDF could be parsed algorithmically into the SNF. Next steps include parsing other drug vocabularies included in the UMLS Metathesaurus and performing human review of the parsed vocabularies. After machine parsed forms have been merged in the Metathesaurus Information Database (MID), editors will be able to edit matched SNFs for accuracy and establish relationships and relationship attributes with other clinical drug concepts.

Algorithms↗