A reliability study for evaluating information extraction from radiology reports. (1/1342)

GOAL: To assess the reliability of a reference standard for an information extraction task. SETTING: Twenty-four physician raters from two sites and two specialties judged whether clinical conditions were present based on reading chest radiograph reports. METHODS: Variance components, generalizability (reliability) coefficients, and the number of expert raters needed to generate a reliable reference standard were estimated. RESULTS: Per-rater reliability averaged across conditions was 0.80 (95% CI, 0.79-0.81). Reliability for the nine individual conditions varied from 0.67 to 0.97, with central line presence and pneumothorax the most reliable, and pleural effusion (excluding CHF) and pneumonia the least reliable. One to two raters were needed to achieve a reliability of 0.70, and six raters, on average, were required to achieve a reliability of 0.95. This was far more reliable than a previously published per-rater reliability of 0.19 for a more complex task. Differences between sites were attributable to changes to the condition definitions. CONCLUSION: In these evaluations, physician raters were able to judge very reliably the presence of clinical conditions based on text reports. Once the reliability of a specific rater is confirmed, it would be possible for that rater to create a reference standard reliable enough to assess aggregate measures on a system. Six raters would be needed to create a reference standard sufficient to assess a system on a case-by-case basis. These results should help evaluators design future information extraction studies for natural language processors and other knowledge-based systems.  (+info)

Mandarin and English single word processing studied with functional magnetic resonance imaging. (2/1342)

The cortical organization of language in bilinguals remains disputed. We studied 24 right-handed fluent bilinguals: 15 exposed to both Mandarin and English before the age of 6 years; and nine exposed to Mandarin in early childhood but English only after the age of 12 years. Blood oxygen level-dependent contrast functional magnetic resonance imaging was performed while subjects performed cued word generation in each language. Fixation was the control task. In both languages, activations were present in the prefrontal, temporal, and parietal regions, and the supplementary motor area. Activations in the prefrontal region were compared by (1) locating peak activations and (2) counting the number of voxels that exceeded a statistical threshold. Although there were differences in the magnitude of activation between the pair of languages, no subject showed significant differences in peak-location or hemispheric asymmetry of activations in the prefrontal language areas. Early and late bilinguals showed a similar pattern of overlapping activations. There are no significant differences in the cortical areas activated for both Mandarin and English at the single word level, irrespective of age of acquisition of either language.  (+info)

A semantic lexicon for medical language processing. (3/1342)

OBJECTIVE: Construction of a resource that provides semantic information about words and phrases to facilitate the computer processing of medical narrative. DESIGN: Lexemes (words and word phrases) in the Specialist Lexicon were matched against strings in the 1997 Metathesaurus of the Unified Medical Language System (UMLS) developed by the National Library of Medicine. This yielded a "semantic lexicon," in which each lexeme is associated with one or more syntactic types, each of which can have one or more semantic types. The semantic lexicon was then used to assign semantic types to lexemes occurring in a corpus of discharge summaries (603,306 sentences). Lexical items with multiple semantic types were examined to determine whether some of the types could be eliminated, on the basis of usage in discharge summaries. A concordance program was used to find contrasting contexts for each lexeme that would reflect different semantic senses. Based on this evidence, semantic preference rules were developed to reduce the number of lexemes with multiple semantic types. RESULTS: Matching the Specialist Lexicon against the Metathesaurus produced a semantic lexicon with 75,711 lexical forms, 22,805 (30.1 percent) of which had two or more semantic types. Matching the Specialist Lexicon against one year's worth of discharge summaries identified 27,633 distinct lexical forms, 13,322 of which had at least one semantic type. This suggests that the Specialist Lexicon has about 79 percent coverage for syntactic information and 38 percent coverage for semantic information for discharge summaries. Of those lexemes in the corpus that had semantic types, 3,474 (12.6 percent) had two or more types. When semantic preference rules were applied to the semantic lexicon, the number of entries with multiple semantic types was reduced to 423 (1.5 percent). In the discharge summaries, occurrences of lexemes with multiple semantic types were reduced from 9.41 to 1.46 percent. CONCLUSION: Automatic methods can be used to construct a semantic lexicon from existing UMLS sources. This semantic information can aid natural language processing programs that analyze medical narrative, provided that lexemes with multiple semantic types are kept to a minimum. Semantic preference rules can be used to select semantic types that are appropriate to clinical reports. Further work is needed to increase the coverage of the semantic lexicon and to exploit contextual information when selecting semantic senses.  (+info)

Automatic identification of pneumonia related concepts on chest x-ray reports. (4/1342)

A medical language processing system called SymText, two other automated methods, and a lay person were compared against an internal medicine resident for their ability to identify pneumonia related concepts on chest x-ray reports. Sensitivity (recall), specificity, and positive predictive value (precision) are reported with respect to an independent panel of physicians. Overall the performance of SymText was similar to the physician and superior to the other methods. The automatic encoding of pneumonia concepts will support clinical research, decision making, computerized clinical protocols, and quality assurance in a radiology department.  (+info)

Mining molecular binding terminology from biomedical text. (5/1342)

Automatic access to information regarding macromolecular binding relationships would provide a valuable resource to the biomedical community. We report on a pilot project to mine such information from the molecular biology literature. The program being developed takes advantage of natural language processing techniques and is supported by two repositories of biomolecular knowledge. A formative evaluation has been conducted on a subset of MEDLINE abstracts.  (+info)

MEDTAG: tag-like semantics for medical document indexing. (6/1342)

Medical documentation is central in health care, as it constitutes the main means of communication between care providers. However, there is a gap to bridge between storing information and extracting the relevant underlying knowledge. We believe natural language processing (NLP) is the best solution to handle such a large amount of textual information. In this paper we describe the construction of a semantic tagset for medical document indexing purposes. Rather than attempting to produce a home-made tagset, we decided to use, as far as possible, standard medicine resources. This step has led us to choose UMLS hierarchical classes as a basis for our tagset. We also show that semantic tagging is not only providing bases for disambiguisation between senses, but is also useful in the query expansion process of the retrieval system. We finally focus on assessing the results of the semantic tagger.  (+info)

Use of the Extensible Stylesheet Language (XSL) for medical data transformation. (7/1342)

Recently, the Extensible Markup Language (XML) has received growing attention as a simple but flexible mechanism to represent medical data. As XML-based markups become more common there will be an increasing need to transform data stored in one XML markup into another markup. The Extensible Stylesheet Language (XSL) is a stylesheet language for XML. Development of a new mammography reporting system created a need to convert XML output from the MEDLee natural language processing system into a format suitable for cross-patient reporting. This paper examines the capability of XSL as a rule specification language that supports the medical XML data transformation. A set of nine relevant transformations was identified: Filtering, Substitution, Specification, Aggregation, Merging, Splitting, Transposition, Push-down and Pull-up. XSL-based methods for implementing these transformations are presented. The strengths and limitations of XSL are discussed in the context of XML medical data transformation.  (+info)

Analysis of biomedical text for chemical names: a comparison of three methods. (8/1342)

At the National Library of Medicine (NLM), a variety of biomedical vocabularies are found in data pertinent to its mission. In addition to standard medical terminology, there are specialized vocabularies including that of chemical nomenclature. Normal language tools including the lexically based ones used by the Unified Medical Language System (UMLS) to manipulate and normalize text do not work well on chemical nomenclature. In order to improve NLM's capabilities in chemical text processing, two approaches to the problem of recognizing chemical nomenclature were explored. The first approach was a lexical one and consisted of analyzing text for the presence of a fixed set of chemical segments. The approach was extended with general chemical patterns and also with terms from NLM's indexing vocabulary, MeSH, and the NLM SPECIALIST lexicon. The second approach applied Bayesian classification to n-grams of text via two different methods. The single lexical method and two statistical methods were tested against data from the 1999 UMLS Metathesaurus. One of the statistical methods had an overall classification accuracy of 97%.  (+info)