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PubMed2005

Abbreviation and acronym disambiguation in clinical discourse.

Use of abbreviations and acronyms is pervasive in clinical reports despite many efforts to limit the use of ambiguous and unsanctioned abbreviations and acronyms. Due to the fact that many abbreviations and acronyms are ambiguous with respect to their sense, complete and accurate text analysis is impossible without identification of the sense that was intended for a given abbreviation or acronym. We present the results of an experiment where we used the contexts harvested from the Internet through Google API to collect contextual data for a set of 8 acronyms found in clinical notes at the Mayo Clinic. We then used the contexts to disambiguate the sense of abbreviations in a manually annotated corpus.

Abbreviations as Topic↗
PubMed2005

How students and faculty interact with a searchable online database of the medical curriculum.

Many medical schools currently provide electronic access to their medical curriculum. In order to better develop and evaluate online curricular databases, knowledge of the interaction of students and faculty with such systems is required. The KnowledeMap application provides a web interface for students, faculty and administrators to perform NLP-assisted searches for documents from the entire medical curriculum. The pilot implementation of KM in a first year anatomy course was evaluated. Data was collected from the web-server log files over two years, a paper survey at the end of the course, and structured interviews with students and faculty members. The data showed complete adoption of KM. Analysis of usage patterns showed that most of the students chose to browse for current course material rather than to search for related medical concepts in future courses. Analysis of the interviews identified key concepts relating to the students' utilization of KM for their learning tasks. The impact of KM on medical pedagogy is discussed in light of our results.

Anatomy↗
PubMed2005

Effective grading of termhood in biomedical literature.

The ever-increasing amount of textual information in biomedicine calls for effective procedures for automatic terminology extraction which assist biomedical researchers and professionals in gathering and organizing terminological knowledge encoded in text documents. In this study, we propose a new, linguistically grounded measure for automatically identifying multi-word terms from the biomedical literature. Our approach is based on the limited paradigmatic modifiability of terms and is tested on bigram, trigram and quadgram noun phrases extracted from a 104-million-word text corpus comprised of Medline abstracts. Using the UMLS Metathesaurus as a gold standard, we show that our algorithm substantially outperforms the standard term identification measures and, therefore, qualifies as a high-performing building block for any biomedical terminology mining system.

Algorithms↗
PubMed2005

Medical textbook summarization and guided navigation using statistical sentence extraction.

We present a method for automated medical textbook and encyclopedia summarization. Using statistical sentence extraction and semantic relationships, we extract sentences from text returned as part of an existing textbook search (similar to a book index). Our system guides users to the information they desire by summarizing the content of each relevant chapter or section returned in the search. The summary is tailored to contain sentences that specifically address the user's search terms. Our clustering method selects sentences that contain concepts specifically addressing the context of the query term in each of the returned sections. Our method examines conceptual relationships from the UMLS and selects clusters of concepts using Expectation Maximization (EM). Sentences associated with the concept clusters are shown to the user. We evaluated whether our extracted summary provides a suitable answer to the user's question.

Abstracting and Indexing↗
PubMed2005

A study of lexical behaviour of sentences in chest radiology reports.

Statistical natural language processors have been the focus of much research during the past decade. The UCLA Medical Imaging Informatics Group (MII) has developed a statistical NLP for the domain of radiology. We report a study of syntactic and semantic behaviour of sentences in the domain of chest radiology.

Humans↗
PubMed2005

Syntactic parsing of medical reports using evolutionary optimization.

We report on a syntactic parser for medical reports which uses a genetic algorithm to efficiently identify the highest ranking parse configuration based on a scoring scheme developed as part of our prior work. The approach was tested on a set of 250 sentences from the domain of radiology. Performance time and comparison to exhaustive methods are given.

Algorithms↗
PubMed2005

A web application to support consumer health vocabulary development.

We describe a Web application that supports collaborative development of a consumer health vocabulary. It performs text analyses and enables distributed human review. It also provides on-the-fly summary reports and facilitates the generation of a final vocabulary based on the results of the review.

Health Services↗
PubMed2005

MeSH Speller + askMEDLINE: auto-completes MeSH terms then searches MEDLINE/PubMed via free-text, natural language queries.

Medical terminology is challenging even for healthcare personnel. Spelling errors can make searching MEDLINE/PubMed ineffective. We developed a utility that provides MeSH term and Specialist Lexicon Vocabulary suggestions as it is typed on a search page. The correctly spelled term can be incorporated into a free-text, natural language search or used as a clinical queries search.

Information Storage and Retrieval↗
PubMed2005

A grammar-based classification of negations in clinical radiology reports.

We describe the development of a classification scheme of negations based on syntactical categories of negative words, and patterns of negation. Each category contains a negative word or phrase and a pattern to locate negated terms. The classification was derived through literature study and manual inspection of 500 radiology reports of six common imaging modalities.

Abstracting and Indexing↗
PubMed2006

Evaluation of lexical methods for detecting relationships between concepts from multiple ontologies.

We used exact term matching, stemming, and inclusion of synonyms, implemented via the Lucene information retrieval library, to discover relationships between the Gene Ontology and three other OBO ontologies: ChEBI, Cell Type, and BRENDA Tissue. Proposed relationships were evaluated by domain experts. We discovered 91,385 relationships between the ontologies. Various methods had a wide range of correctness. Based on these results, we recommend careful evaluation of all matching strategies before use, including exact string matching. The full set of relationships is available at compbio.uchsc.edu/dependencies.

Computational Biology↗
PubMed2006

Nursing spoken corpora for understanding nursing assignments.

We have built Spoken Corpora using voice data that were obtained by monitored and recorded daily nursing assignments in hospitals. The devices, we have developed are able to monitor daily nursing assignments and record multimedia data, such as video, voice, pedometer, acceleration, and. so on. Focusing on voice data, we have built two types of corpora one for analyzing nursing tasks and the other for collecting nursing terms. This work represents the first trail in Japan to develop such special devices and to make corpora for understanding nursing assignments. In this paper, we report our experiments done in "E-nightingale Project," present an effective task analysis of nursing activities, and describe results of an analysis of nursing terms for developing "Automatic Time Study Survey System."

Communication↗
PubMed2006

Sifting abstracts from Medline and evaluating their relevance to molecular biology.

The most important knowledge in the area of biology currently consists of raw text documents. Bibliographic databases of biomedical articles can be searched, but an efficient procedure should evaluate the relevance of documents to biology. In genetics, this challenge is even trickier, because of the lack of consistency in genes' naming tradition. We aim to define a good approach for collecting relevant abstracts for biology and for studied species and genes. Our approach relies on defining best queries, detecting and filtering best sources.

France↗
PubMed2006

Referent tracking: the problem of negative findings.

The paradigm of referent tracking is based on a realist presupposition which rejects so-called negative entities (congenital absent nipple, and the like) as spurious. How, then, can a referent tracking-based Electronic Health Record deal with what are standardly called 'negative findings'? To answer this question we carried out an analysis of some 748 sentences drawn from patient charts and containing some form of negation. Our analysis shows that to deal with these sentences we need to introduce a new ontological relationship between a particular and a universal, which holds when no instance of the universal has a specific qualified ontological relation with the particular. This relation is found to be able to accommodate nearly all occurrences of negative findings in the examined sample, in ways which involve no reference to negative entities.

Forms and Records Control↗
PubMed2006

Enriching medical terminologies: an approach based on aligned corpora.

Medical terminologies such as those in the UMLS are never exhaustive and there is a constant need to enrich them, especially in terms of multilinguality. We present a methodology to acquire new French translations of English medical terms based on word alignment in a parallel corpus - i.e. pairing of corresponding words. We automatically collected a 27.7-million-word parallel, English-French corpus. Based on a first 1.3-million-word extract of this corpus, we detected 10,171 candidate French translations of English medical terms from MeSH and SNOMED, among which 3,807 are new translations of English MeSH terms.

France↗
PubMed2006

Knowledge acquisition for computation of semantic distance between WHO-ART terms.

Computation of semantic distance between adverse drug reactions terms may be an efficient way to group related medical conditions in pharmacovigilance case reports. Previous experience with ICD-10 on a semantic distance tool highlighted a bottleneck related to manual description of formal definitions in large terminologies. We propose a method based on acquisition of formal definitions by knowledge extraction from UMLS and morphosemantic analysis. These formal definitions are expressed with SNOMED International terms. We provide formal definitions for 758 WHO-ART terms: 321 terms defined from UMLS, 320 terms defined using morphosemantic analysis and 117 terms defined after expert evaluation. Computation of semantic distance (e.g. k-nearest neighbours) was implemented in J2EE terminology services. Similar WHO-ART terms defined by automated knowledge acquisition and ICD terms defined manually show similar behaviour in the semantic distance tool. Our knowledge acquisition method can help us to generate new formal definitions of medical terms for our semantic distance terminology services.

Adverse Drug Reaction Reporting Systems↗
PubMedDate not supplied

Using the UMLS to bring the library to the bedside.

This paper presents an algorithm that can be used to convert ICD9 terms to related MeSH terms. Preliminary evaluation indicates that together, the algorithm and the UMLS provide a reasonable resource for facilitating such conversions.

Algorithms↗