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PubMedDate not supplied

A semantic lexicon for medical language processing.

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.

Humans↗
PubMed2000

Medical document anonymization with a semantic lexicon.

We present an original system for locating and removing personally-identifying information in patient records. In this experiment, anonymization is seen as a particular case of knowledge extraction. We use natural language processing tools provided by the MEDTAG framework: a semantic lexicon specialized in medicine, and a toolkit for word-sense and morpho-syntactic tagging. The system finds 98-99% of all personally-identifying information.

Computer Security↗
PubMed1998

Identification of anatomical terminology in medical text.

We report on an experiment to use the natural language processing tools being developed in the SPECIALIST system to accurately identify terminology associated with the coronary arteries as expressed in coronary catheterization reports. The ultimate goal is to map from any anatomically-oriented medical text to online images, using the UMLS as an intermediate knowledge source. We describe some of the problems encountered when processing coronary artery terminology and report on the results of a formative evaluation of a tool for addressing these problems.

Cardiac Catheterization↗
PubMed1999

MEDTAG: tag-like semantics for medical document indexing.

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.

Abstracting and Indexing↗
PubMed2003

Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation.

MOTIVATION: Searching relevant publications for manual database annotation is a tedious task. In this paper, we apply a combination of Natural Language Processing (NLP) and probabilistic classification to re-rank documents returned by PubMed according to their relevance to Swiss-Prot annotation, and to identify significant terms in the documents. RESULTS: With a Probabilistic Latent Categoriser (PLC) we obtained 69% recall and 59% precision for relevant documents in a representative query. As the PLC technique provides the relative contribution of each term to the final document score, we used the Kullback-Leibler symmetric divergence to determine the most discriminating words for Swiss-Prot medical annotation. This information should allow curators to understand classification results better. It also has great value for fine-tuning the linguistic pre-processing of documents, which in turn can improve the overall classifier performance.

Abstracting and Indexing↗
PubMed2001

Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.

The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.

Abstracting and Indexing↗
PubMed2003

Extraction of protein interaction information from unstructured text using a context-free grammar.

MOTIVATION: As research into disease pathology and cellular function continues to generate vast amounts of data pertaining to protein, gene and small molecule (PGSM) interactions, there exists a critical need to capture these results in structured formats allowing for computational analysis. Although many efforts have been made to create databases that store this information in computer readable form, populating these sources largely requires a manual process of interpreting and extracting interaction relationships from the biological research literature. Being able to efficiently and accurately automate the extraction of interactions from unstructured text, would greatly improve the content of these databases and provide a method for managing the continued growth of new literature being published. RESULTS: In this paper, we describe a system for extracting PGSM interactions from unstructured text. By utilizing a lexical analyzer and context free grammar (CFG), we demonstrate that efficient parsers can be constructed for extracting these relationships from natural language with high rates of recall and precision. Our results show that this technique achieved a recall rate of 83.5% and a precision rate of 93.1% for recognizing PGSM names and a recall rate of 63.9% and a precision rate of 70.2% for extracting interactions between these entities. In contrast to other published techniques, the use of a CFG significantly reduces the complexities of natural language processing by focusing on domain specific structure as opposed to analyzing the semantics of a given language. Additionally, our approach provides a level of abstraction for adding new rules for extracting other types of biological relationships beyond PGSM relationships. AVAILABILITY: The program and corpus are available by request from the authors.

Algorithms↗
PubMed2004

Term identification in the biomedical literature.

Sophisticated information technologies are needed for effective data acquisition and integration from a growing body of the biomedical literature. Successful term identification is key to getting access to the stored literature information, as it is the terms (and their relationships) that convey knowledge across scientific articles. Due to the complexities of a dynamically changing biomedical terminology, term identification has been recognized as the current bottleneck in text mining, and--as a consequence--has become an important research topic both in natural language processing and biomedical communities. This article overviews state-of-the-art approaches in term identification. The process of identifying terms is analysed through three steps: term recognition, term classification, and term mapping. For each step, main approaches and general trends, along with the major problems, are discussed. By assessing previous work in context of the overall term identification process, the review also tries to delineate needs for future work in the field.

Abbreviations as Topic↗
PubMed2004

MedBlast: searching articles related to a biological sequence.

UNLABELLED: In the genomic era, researchers often want to know more information about a biological sequence by retrieving its related articles. However, there is no available tool yet to achieve conveniently this goal. Here we developed a new literature-mining tool MedBlast, which uses natural language processing techniques, to retrieve the related articles of a given sequence. An online server of this program is also provided. AVAILABILITY: Both online server and the program are available freely at http://medblast.sibsnet.org

Algorithms↗
PubMed2003

Interpreting hypernymic propositions in an online medical encyclopedia.

Interpretation of semantic propositions from bio-medical texts documents would provide valuable support to natural language processing (NLP) applications. We are developing a methodology to interpret a kind of semantic proposition, the hypernymic proposition, in MEDLINE abstracts. In this paper, we expanded the system to identify these structures in a different discourse domain: the Medical Encyclopedia from the National Library of Medi-cine's MEDLINEplus Website.

Encyclopedias as Topic↗
PubMed2003

Semantic relations asserting the etiology of genetic diseases.

Considerable research is being directed at extracting molecular biology information from text. Particularly challenging in this regard is to identify relations between entities, such as protein-protein interactions or molecular pathways. In this paper we present a natural language processing method for extracting causal relations between genetic phenomena and diseases. After presenting the results of preliminary evaluation, we suggest the use of a graphical display application for viewing the semantic predications produced by the system.

Computer Graphics↗
PubMed2005

Building and using a medical ontology for knowledge management and cooperative work in a health care network.

In the context of a health care network, we describe our method for reconstitution of a medical ontology via the translation of a medical database (DB) towards RDF(S) language. Then we show how we extended this ontology among others through natural language processing of a textual corpus. Then, we present the construction of a tool called "Virtual Staff", enabling a cooperative diagnosis by some of the health care network actors, by relying on this medical ontology and on the creation of SOAP and QOC graphs.

Algorithms↗
PubMed2000

Semantic analysis of medical free texts.

The semantic interpretation of natural language utterances is usually based on a large number of transformation rules which map syntactic structures (parse trees) onto some kind of meaning representation. However, those interpretation rules exhibit an insufficient degree of abstraction so that the scalability and portability of such natural language processing systems is hard to maintain. In this paper, we introduce an approach that is able to cope with a wide variety of semantic interpretation patterns in medical free texts by applying a small inventory of abstract semantic interpretation schemata. These schemata address generalized graph configurations within syntactic dependency parse trees, which abstract away from specific syntactic constructions.

Computer Graphics↗
PubMed2005

Indexing anatomical phrases in neuro-radiology reports to the UMLS 2005AA.

This work describes a methodology to index anatomical phrases to the 2005AA release of the Unified Medical Language System (UMLS). A phrase chunking tool based on Natural Language Processing (NLP) was developed to identify semantically coherent phrases within medical reports. Using this phrase chunker, a set of 2,551 unique anatomical phrases was extracted from brain radiology reports. These phrases were mapped to the 2005AA release of the UMLS using a vector space model. Precision for the task of indexing unique phrases was 0.87.

Abstracting and Indexing↗
PubMed2005

Building medical ontologies by terminology extraction from texts: an experiment for the intensive care units.

In many medical fields, maintenance, comparison and aggregation of unambiguous terminologies go through formal specialized clinical terminologies: ontologies. We describe a methodology to build medical ontology from textual reports using a natural language processing tool, the SYNTEX software. The methodology is illustrated in the surgical intensive care medical domain. We have tested the possibility for an expert to build a sizeable ontology in a reasonable time. The quality of the ontology has been evaluated according to its capacity to cover the ICD-10 terminology in the field. Finally, the methodology itself is discussed.

Humans↗
PubMed2005

Desiderata for representing anatomical knowledge.

The general problem of knowledge representation for gross anatomy supporting both computers and human is rarely globally solved. Partial solutions are flourishing, but the actual and potential users are left with a lack of satisfaction and uncomfortable feeling of incompleteness. Moreover, these solutions are not ready for a sound evolution and are at risk to disappear at any moment by default of adequate maintenance. In addition, the problem is complicated by the fact that any solutions should be relevant for Natural Language Processing applications in a multilingual environment.This paper tackles with this problem and defines the basic steps for a proper knowledge representation scheme. Taking the subdomain of gross anatomy, it shows how each step has been solved and what performances and benefits are expected by such a solution. A discussion is done on the way to interface from a common source for both computers and humans.

Anatomy↗
PubMed2002

Terminology extraction from text to build an ontology in surgical intensive care.

In many medical fields, the maintenance of unabiguous terminologies, the comparison and aggregation of different terminologies go through the building of formal specialized clinical terminologies, the ontologies. In this paper, we describe the building of an ontology in the surgical intensive care medical domain. We considered textual reports as the main source of information and a natural language processing tool, the SYNTEX software, is used to build the ontology. We have tested the possibility for an expert to build a sizeable ontology in a reasonable time. The quality of the ontology has been evaluated according to its capacity to cover the ICD-10 terminology in the field. Examples of coding activity with the ontology are proposed and discussed.

Critical Care↗
PubMed2003

Kinase pathway database: an integrated protein-kinase and NLP-based protein-interaction resource.

Protein kinases play a crucial role in the regulation of cellular functions. Various kinds of information about these molecules are important for understanding signaling pathways and organism characteristics. We have developed the Kinase Pathway Database, an integrated database involving major completely sequenced eukaryotes. It contains the classification of protein kinases and their functional conservation, ortholog tables among species, protein-protein, protein-gene, and protein-compound interaction data, domain information, and structural information. It also provides an automatic pathway graphic image interface. The protein, gene, and compound interactions are automatically extracted from abstracts for all genes and proteins by natural-language processing (NLP). The method of automatic extraction uses phrase patterns and the GENA protein, gene, and compound name dictionary, which was developed by our group. With this database, pathways are easily compared among species using data with more than 47,000 protein interactions and protein kinase ortholog tables. The database is available for querying and browsing at http://kinasedb.ontology.ims.u-tokyo.ac.jp/.

Animals↗