SUBJECT HEADINGS IN THE WELLCOME HISTORICAL MEDICAL LIBRARY.
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The strength and unity of the medical library profession spring largely from the existence of the National Library of Medicine. Its publications promise to bring much closer the standardization and consistency still lacking to the world of medical subject headings. The Wellcome Library headings are similar to those listed in the National Library of Medicine Subject Heading Authority List of 1960. However, references and additional headings are made to cover (1) superseded terms, (2) historical phenomena with distinctive names which are peculiar to an epoch, (3) subjects from social, religious, or other branches of history, and (4) eponyms. Old texts often describe disease ambiguously or use terminology which has since been superseded. The catalogue is in three parts-topographical, biographical, and topical. The quarterly Current Work has certain simplifications (e.g., elimination of some subheadings), and has recently introduced references both to cut down needless duplication and to facilitate consultation by the reader. (+info)
Assessing explicit error reporting in the narrative electronic medical record using keyword searching.
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BACKGROUND: Many types of medical errors occur in and outside of hospitals, some of which have very serious consequences and increase cost. Identifying errors is a critical step for managing and preventing them. In this study, we assessed the explicit reporting of medical errors in the electronic record. METHOD: We used five search terms "mistake," "error," "incorrect," "inadvertent," and "iatrogenic" to survey several sets of narrative reports including discharge summaries, sign-out notes, and outpatient notes from 1991 to 2000. We manually reviewed all the positive cases and identified them based on the reporting of physicians. RESULT: We identified 222 explicitly reported medical errors. The positive predictive value varied with different keywords. In general, the positive predictive value for each keyword was low, ranging from 3.4 to 24.4%. Therapeutic-related errors were the most common reported errors and these reported therapeutic-related errors were mainly medication errors. CONCLUSION: Keyword searches combined with manual review indicated some medical errors that were reported in medical records. It had a low sensitivity and a moderate positive predictive value, which varied by search term. Physicians were most likely to record errors in the Hospital Course and History of Present Illness sections of discharge summaries. The reported errors in medical records covered a broad range and were related to several types of care providers as well as non-health care professionals. (+info)
The Unified Medical Language System (UMLS): integrating biomedical terminology.
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The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900,000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP. (+info)
GIS: a biomedical text-mining system for gene information discovery.
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We present a biomedical text-mining system focused on four types of gene-related information: biological functions, associated diseases, related genes and gene-gene relations. The aim of this system is to provide researchers an easy-to-use bio-information service that will rapidly survey the rapidly burgeoning biomedical literature. AVAILABILITY: http://iir.csie.ncku.edu.tw/~yuhc/gis/ (+info)
Cross-language MeSH indexing using morpho-semantic normalization.
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We consider three alternative procedures for the automatic indexing of medical documents using MeSH thesaurus identifiers as target units (document descriptors). Rather than considering complete words as the starting point of the indexing procedure, we here propose morphologically plausible subwords as basic units from which MeSH terms are derived. We describe the morphological segmentation and normalization procedures, as well as the mappings from subwords to MeSH terms, and discuss results from an evaluation carried out on a German-language corpus. (+info)
Designing metaschemas for the UMLS enriched semantic network.
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The enriched semantic network (ESN) has previously been presented as an enhancement of the semantic network (SN) of the UMLS. The ESN's hierarchy is a DAG (Directed Acyclic Graph) structure allowing for multiple parents. The ESN is thus more complex than the SN and can be more difficult to view and comprehend. We have previously introduced the notion of a metaschema for the SN as a compact abstraction to support SN comprehension. We extend the definition of metaschema to make it applicable to a DAG classification hierarchy, such as the one exhibited by the ESN. We specify the requirements for and describe the general process of deriving such a metaschema. We derive two particular metaschemas of the ESN based on a pair of partitions. These two metaschemas and their underlying partitions are compared. Both metaschemas serve as compact representations of the ESN, allowing for convenient viewing of its hierarchy and easier comprehension. (+info)
Consistency across the hierarchies of the UMLS Semantic Network and Metathesaurus.
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OBJECTIVE: To develop and test a method for automatically detecting inconsistencies between the parent-child is-a relationships in the Metathesaurus and the ancestor-descendant relationships in the Semantic Network of the Unified Medical Language System (UMLS). METHODS: We exploited the fact that each Metathesaurus concept is assigned one or more semantic types from the UMLS Semantic Network and that the semantic types are arranged in a hierarchy. We compared the semantic types of each pair of parent and child concepts to determine if the types "explained" the Metathesaurus is-a relationships. We considered cases where the semantic type of the parent was neither the same as, nor an ancestor of, the semantic type of the child to be "unexplained." We applied this method to the January 2002 release of the UMLS and examined the unexplained cases we discovered to determine their causes. RESULTS: We found that 17022 (24.3%) of the parent-child is-a relationships in the UMLS Metathesaurus could not be explained based on the semantic types of the concepts. Causes for these discrepancies included cases where the parent or child was missing a semantic type, cases where the semantic type of the child was too general or the semantic type of the parent was too specific, cases where the parent-child relationship was incorrect, and cases where an ancestor-descendant relationship should be added to the UMLS Semantic network. In many cases, the specific cause of the discrepancy cannot be resolved without authoritative judgment by the UMLS developers. CONCLUSIONS: Our method successfully detects inconsistencies between the hierarchies of the UMLS Metathesaurus and Semantic Network. We believe that our method should be added to the set of tools that the UMLS developers use to maintain and audit the UMLS knowledge sources. (+info)
The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text.
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Interpretation of semantic propositions in free-text documents such as MEDLINE citations would provide valuable support for biomedical applications, and several approaches to semantic interpretation are being pursued in the biomedical informatics community. In this paper, we describe a methodology for interpreting linguistic structures that encode hypernymic propositions, in which a more specific concept is in a taxonomic relationship with a more general concept. In order to effectively process these constructions, we exploit underspecified syntactic analysis and structured domain knowledge from the Unified Medical Language System (UMLS). After introducing the syntactic processing on which our system depends, we focus on the UMLS knowledge that supports interpretation of hypernymic propositions. We first use semantic groups from the Semantic Network to ensure that the two concepts involved are compatible; hierarchical information in the Metathesaurus then determines which concept is more general and which more specific. A preliminary evaluation of a sample based on the semantic group Chemicals and Drugs provides 83% precision. An error analysis was conducted and potential solutions to the problems encountered are presented. The research discussed here serves as a paradigm for investigating the interaction between domain knowledge and linguistic structure in natural language processing, and could also make a contribution to research on automatic processing of discourse structure. Additional implications of the system we present include its integration in advanced semantic interpretation processors for biomedical text and its use for information extraction in specific domains. The approach has the potential to support a range of applications, including information retrieval and ontology engineering. (+info)