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PubMed1994

Global information infrastructure.

The High Performance Computing and Communications Program (HPCC) is a multiagency federal initiative under the leadership of the White House Office of Science and Technology Policy, established by the High Performance Computing Act of 1991. It has been assigned a critical role in supporting the international collaboration essential to science and to health care. Goals of the HPCC are to extend USA leadership in high performance computing and networking technologies; to improve technology transfer for economic competitiveness, education, and national security; and to provide a key part of the foundation for the National Information Infrastructure. The first component of the National Institutes of Health to participate in the HPCC, the National Library of Medicine (NLM), recently issued a solicitation for proposals to address a range of issues, from privacy to 'testbed' networks, 'virtual reality,' and more. These efforts will build upon the NLM's extensive outreach program and other initiatives, including the Unified Medical Language System (UMLS), MEDLARS, and Grateful Med. New Internet search tools are emerging, such as Gopher and 'Knowbots'. Medicine will succeed in developing future intelligent agents to assist in utilizing computer networks. Our ability to serve patients is so often restricted by lack of information and knowledge at the time and place of medical decision-making. The new technologies, properly employed, will also greatly enhance our ability to serve the patient.

Computer Communication Networks↗

PubMed1992

Integrating the UMLS into VNS Retriever.

We are developing a networked resource for the National Library of Medicine's Unified Medical Language System. We call this resource the UMLS Retriever, which is an instance of our VNS Retriever architecture. Our prototype user interface makes use of the Virtual Notebook System Browser. The development of a networked UMLS service will result in numerous advantages to our user community.

Computer Communication Networks↗

PubMed2003

OQAFMA Querying agent for the Foundational Model of Anatomy: a prototype for providing flexible and efficient access to large semantic networks.

The development of large semantic networks, such as the UMLS, which are intended to support a variety of applications, requires a flexible and efficient query interface for the extraction of information. Using one of the source vocabularies of UMLS as a test bed, we have developed such a prototype query interface. We first identify common classes of queries needed by applications that access these semantic networks. Next, we survey StruQL, an existing query language that we adopted, which supports all of these classes of queries. We then describe the OQAFMA Querying Agent for the Foundational Model of Anatomy (OQAFMA), which provides an efficient implementation of a subset of StruQL by pre-computing a variety of indices. We describe how OQAFMA leverages database optimization by converting StruQL queries to SQL. We evaluate the flexibility and efficiency of our implementation using English queries written by anatomists. This evaluation verifies that OQAFMA provides flexible, efficient access to one such large semantic network, the Foundational Model of Anatomy, and suggests that OQAFMA could be an efficient query interface to other large biomedical knowledge bases, such as the Unified Medical Language System.

Abstracting and Indexing↗

PubMed1992

Representation of clinical data using SNOMED III and conceptual graphs.

None of the coding schemes currently contained within the Unified Medical Language System (UMLS) is sufficiently expressive to represent medical progress notes adequately. Some coding schemes suffer from domain incompleteness, others suffer from the inability to represent modifiers and time references, and some suffer from both problems. The recently released version of the Systematized Nomenclature of Medicine (SNOMED III) is a potential solution to the data-representation problem because it is relatively domain complete, and because it uses a generative coding scheme that will allow the construction of codes that contain modifiers and time references. SNOMED III does have an important weakness, however. SNOMED III lacks a formalized system for using its codes; thus, it fails to ensure consistency in its use across different institutions. Application of conceptual-graph formalisms to SNOMED III can ensure such consistency of use. Conceptual-graph formalisms will also allow mapping of the resulting SNOMED III codes onto relational data models and onto other formal systems, such as first-order predicate calculus.

Medical Informatics Applications↗

PubMed1998

ARIANE: integration of information databases within a hospital intranet.

Large information systems handle massive volume of data stored in heterogeneous sources. Each server has its own model of representation of concepts with regard to its aims. One of the main problems end-users encounter when accessing different servers is to match their own viewpoint on biomedical concepts with the various representations that are made in the databases servers. The aim of the project ARIANE is to provide end-users with easy-to-use and natural means to access and query heterogeneous information databases. The objectives of this research work consist in building a conceptual interface by means of the Internet technology inside an enterprise Intranet and to propose a method to realize it. This method is based on the knowledge sources provided by the Unified Medical Language System (UMLS) project of the US National Library of Medicine. Experiments concern queries to three different information servers: PubMed, a Medline server of the NLM; Thériaque, a French database on drugs implemented in the Hospital Intranet; and a Web site dedicated to Internet resources in gastroenterology and nutrition, located at the Faculty of Medicine of Nice (France). Accessing to each of these servers is different according to the kind of information delivered and according to the technology used to query it. Dealing with health care professional workstation, the authors introduced in the ARIANE project quality criteria in order to attempt a homogeneous and efficient way to build a query system able to be integrated in existing information systems and to integrate existing and new information sources.

Computer Communication Networks↗

PubMed2004

Mapping various information sources to a semantic network.

Giessen Data Dictionary Server (GDDS) provides context sensitive information services to disparate clinical applications by automatically navigating a semantic network that stores medical knowledge. Mapping multiple information sources to single clinical application becomes a challenge due to different organization of semi-structured information sources. Linking huge unstructured information sources such as medical literature is even more challenging because we need to develop a mechanism to organize unstructured information and take into account the scalability issue. In this paper, we have successfully mapped two drug information sources by developing an independent subnet for each source and linking them at proper nodes. For medical literature, we have demonstrated that the semantic network of the Unified Medical Language Systems and human assigned topics to each document can be used to organize the large amount of medical literature into the framework of the GDDS service.

Dictionaries, Medical as Topic↗

PubMed2000

Digital library research and application.

The last decade has seen an exponential rise in the amount and the nature of information that is available on the Internet. Internet and World Wide Web technology have raised many technical as well as social and economic issues. The paper begins with a description of the recent digital library research initiative funded by the U.S. federal government. Digital library research addresses a host of informatics issues relevant to the current Internet-based environment. The paper continues with a discussion of some problems in information access, including a discussion of the potential of the Unified Medical Language System for navigating among multiple biomedical information systems. Next, some issues in creating Web-accessible resources for health care consumers are addressed, with a focus on recent work at the National Library of Medicine, including a description of the MEDLINEplus system. The paper ends with a brief discussion of some implications of digital library research for the health informatics community.

Internet↗

PubMed2003

Exploring semantic groups through visual approaches.

Objectives. We investigate several visual approaches for exploring semantic groups, a grouping of semantic types from the Unified Medical Language System (UMLS) semantic network. We are particularly interested in the semantic coherence of the groups, and we use the semantic relationships as important indicators of that coherence. Methods. First, we create a radial representation of the number of relationships among the groups, generating a profile for each semantic group. Second, we show that, in our partition, the relationships are organized around a limited number of pivot groups and that partitions created at random do not exhibit this property. Finally, we use correspondence analysis to visualize groupings resulting from the association between semantic types and the relationships. Results. The three approaches provide different views on the semantic groups and help detect potential inconsistencies. They make outliers immediately apparent, and, thus, serve as a tool for auditing and validating both the semantic network and the semantic groups.

Abstracting and Indexing↗

PubMed2006

A system for automated lexical mapping.

OBJECTIVE: To automate the mapping of disparate databases to standardized medical vocabularies. BACKGROUND: Merging of clinical systems and medical databases, or aggregation of information from disparate databases, frequently requires a process whereby vocabularies are compared and similar concepts are mapped. DESIGN: Using a normalization phase followed by a novel alignment stage inspired by DNA sequence alignment methods, automated lexical mapping can map terms from various databases to standard vocabularies such as the UMLS (Unified Medical Language System) and LOINC (Logical Observation Identifier Names and Codes). MEASUREMENTS: This automated lexical mapping was evaluated using three real-world laboratory databases from different health care institutions. The authors report the sensitivity, specificity, percentage correct (true positives plus true negatives divided by total number of terms), and true positive and true negative rates as measures of system performance. RESULTS: The alignment algorithm was able to map 57% to 78% (average of 63% over all runs and databases) of equivalent concepts through lexical mapping alone. True positive rates ranged from 18% to 70%; true negative rates ranged from 5% to 52%. CONCLUSION: Lexical mapping can facilitate the integration of data from diverse sources and decrease the time and cost required for manual mapping and integration of clinical systems and medical databases.

Algorithms↗

PubMed2005

Analysis of polarity information in medical text.

Knowing the polarity of clinical outcomes is important in answering questions posed by clinicians in patient treatment. We treat analysis of this information as a classification problem. Natural language processing and machine learning techniques are applied to detect four possibilities in medical text: no outcome, positive outcome, negative outcome, and neutral outcome. A supervised learning method is used to perform the classification at the sentence level. Five feature sets are constructed: unigrams, bigrams, change phrases, negations, and categories. The performance of different combinations of feature sets is compared. The results show that generalization using the category information in the domain knowledge base Unified Medical Language System is effective in the task. The effect of context information is significant. Combining linguistic features and domain knowledge leads to the highest accuracy.

Artificial Intelligence↗

PubMed1998

A proposal for incorporating health level seven (HL7) vocabulary in the UMLS Metathesaurus.

The HL7 Vocabulary Technical Committee (TC) was organized to select and maintain the vocabulary used in HL7 messages. The goal is to make implementations of the Version 3 HL7 Standard more plug-and-play compatible. In order to make the vocabulary readily accessible to the public, HL7 is collaborating with the U.S. National Library of Medicine (NLM) to include HL7 vocabulary in the Unified Medical Language System (UMLS) Metathesaurus. This article describes a proposal for how HL7 data elements and coded values can be represented accurately in the relational tables of the UMLS Metathesaurus.

Computer Communication Networks↗

PubMed2003

The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text.

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.

Abstracting and Indexing↗

PubMed2005

Amplification of Terminologia anatomica by French language terms using Latin terms matching algorithm: a prototype for other language.

OBJECTIVE: Terminologia anatomica is the new standard in anatomical terminology. This terminology is available only in Latin and English and its worldwide adoption is subject to the addition of terms from others languages. On the other hand, Nomina anatomica, the previous standard, has been widely translated. Aim of this work was to append foreign terms to Terminologia by using similarity-matching algorithm between its Latin terms and those from Nomina. METHODS: A semi-automatic matching of Latin terms from Terminologia with those of Nomina was performed using a string-to-string distance algorithm and manual assessment. We used a French-Latin version of Nomina together with Terminologia and we suggested French terms for Terminologia. Coverage was evaluated by the number of exact and approximate matches. A target of 78% was set due to the higher number of terms in Terminologia compared to Nomina. Relevance was estimated by manually comparing the meanings of the English and French terms related to the same Latin term. The question was whether they refer to the same anatomical structure. RESULTS: Exact or approximate matches were found for 5982 terms (76.5%) of Terminologia. Our results indicated that more than 75% of the terms from Terminologia came from Nomina, most of them were left unchanged and all were used with the same meaning. CONCLUSION: This method produces relevant results, reaching our 78% target. The method is based only on Latin terms and can be used for other languages. We consider this work as a starting point for adding terms to other knowledge sources, such as the foundational model of anatomy or the Unified Medical Language System (UMLS).

Algorithms↗

PubMed2006

Using UMLS to map from a library to a clinical classification: Improving the functionality of a digital library.

The Metathesaurus of the Unified Medical Language System (UMLS) offers the possibility of mapping between various medical vocabularies. The Primary Care Electronic Library (PCEL) contains a database of over six thousand Medical Subject Headings (MeSH terms) describing the resources of the electronic library. We were interested to know if it was possible to map from MeSH to the Systemized Nomenclature of Medicine Clinical Terms (SNOMED CT). Such a mapping would aid healthcare professionals to retrieve relevant data from our digital library as it would enable links between clinical systems and indexed material.

Databases, Bibliographic↗

PubMed1993

Coach: applying UMLS knowledge sources in an expert searcher environment.

With the development of the Unified Medical Language System (UMLS) Knowledge Sources, the National Library of Medicine (NLM) has produced a resource of great potential for improving the searching of MEDLINE. The Coach expert searcher system, an inhouse research project at NLM, is designed to help users of the GRATEFUL MED front-end software improve MEDLINE search and retrieval capabilities. This paper describes the Coach program, the knowledge sources it uses, and some of the ways it applies elements of the UMLS Metathesaurus to facilitate access to the biomedical literature.

Grateful Med↗

PubMed2003

Consistency across the hierarchies of the UMLS Semantic Network and Metathesaurus.

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.

Abstracting and Indexing↗

PubMed1994

A comparison of four schemes for codification of problem lists.

We set out to evaluate the completeness of four major coding schemes in representation of the patient problem list: the Unified Medical Language System (UMLS, 4th edition), the Systematized Nomenclature of Medicine (SNOMED International), the Read coding system (version 2), and the International Classification of Diseases (9th Clinical Modification)(ICD-9-CM). We gathered 400 problems from patient records at primary care sites in Omaha and Seattle. Matching these against the best description found in each of the coding schemes, we asked five medical faculty reviewers to rate the matches on a five-point Likert scale assessing their satisfaction with the results. For the four schemes, we computed the following rates of dissatisfaction, satisfaction, and average scores: [table: see text] From this analysis, we conclude that UMLS and SNOMED performed substantially better in capturing the clinical content of the problem lists than READ or ICD-9-CM. No scheme could be considered comprehensive. Depending on the goal of systems developers, UMLS and SNOMED may offer different, and complementary, advantages.

Disease↗

PubMed2004

Towards the development of a conceptual distance metric for the UMLS.

The objective of this work is to investigate the feasibility of conceptual similarity metrics in the framework of the Unified Medical Language System (UMLS). We have investigated an approach based on the minimum number of parent links between concepts, and evaluated its performance relative to human expert estimates on three sets of concepts for three terminologies within the UMLS (i.e., MeSH, ICD9CM, and SNOMED). The resulting quantitative metric enables computer-based applications that use decision thresholds and approximate matching criteria. The proposed conceptual matching supports problem solving and inferencing (using high-level, generic concepts) based on readily available data (typically represented as low-level, specific concepts). Through the identification of semantically similar concepts, conceptual matching also enables reasoning in the absence of exact, or even approximate, lexical matching. Finally, conceptual matching is relevant for terminology development and maintenance, machine learning research, decision support system development, and data mining research in biomedical informatics and other fields.

Algorithms↗