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Biomedical subjects

N E Olson

Publications and source records attributed to N E Olson.

18 recordsLinked to original sources
PubMed1996

MEME-II supports the cooperative management of terminology.

Health care enterprises need enterprise-wide terminologies to compare, reuse and repurpose health care descriptions. But once they are created, these terminologies need to be maintained and enhanced to sustain their utility and that of the descriptions encoded with them. MEME II (Metathesaurus Enhancement and Maintenance Environment, Version II) supports the required activities and enables enterprises to leverage their investment in terminology and descriptions by permitting remote-extra-enterprise-enhancements to terminology to be incorporated locally, and local-intra-enterprise-enhancements to be shared remotely. MEME II represents all changes to terminologies as data, or "actions," that can be interpreted by an "action engine." These actions, or messages, represent semantic "units of work" that can be interpreted by other copies of MEME II. The exchange of update messages increases the likelihood that the comparability of terminology-based health care descriptions can be sustained.

Information Systems↗
PubMed1996

Toward reusable software components at the point of care.

An architecture built from five software components -a Router, Parser, Matcher, Mapper, and Server -fulfills key requirements common to several point-of-care information and knowledge processing tasks. The requirements include problem-list creation, exploiting the contents of the Electronic Medical Record for the patient at hand, knowledge access, and support for semantic visualization and software agents. The components use the National Library of Medicine Unified Medical Language System to create and exploit lexical closure-a state in which terms, text and reference models are represented explicitly and consistently. Preliminary versions of the components are in use in an oncology knowledge server.

Computer Systems↗
PubMed1995

Categorization of free-text problem lists: an effective method of capturing clinical data.

Problem lists assist in organizing patient information in computer based medical records. However, in order to use problem lists for billing, research, decision support and standardization, a categorization of the problems entered is required. We describe the problem list component of our computerized patient record, the On-line Medical Record (OMR), which combines a free-text entry mechanism with a categorization scheme, using a dictionary containing 846 terms. All 118,040 problems entered during the system's six years of use have been analyzed, 477 clinicians have entered a mean +/- S.D. of 238 +/- 604 problems into 22,311 patient records. The average number of problems in each patient's file was 5.1 +/- 3.9. Comments were typed for 80,281 (68%) of the problems, ranging in length from 1 to 2456 characters, with a mean length of 98 +/- 110 characters. Half the problems were entered on the day of the encounter with the patient. Overall, 66% of all problems were categorized in relation to terms from the problem dictionary. Lexical analysis of all problem names showed that 80% could be mapped to Meta 1.4, Snomed 3.0 or a pre-release version of Read 3.0. We conclude that a problem list entry scheme combining free-text entry and optional categorization using a dictionary can result in a high proportion of problems being categorized as desired. Improvement of the system by elimination of unused dictionary terms and addition of 1000 terms identified by the lexical analysis is likely to result in even higher categorization rates.

Humans↗
PubMed1995

Merging terminologies.

A terminology is a systematic, authoritative collection of concept names, or terms, in some domain. No single terminology names all the important concepts in biomedicine. One approach to creating a more comprehensive biomedical terminology is to merge existing biomedical terminologies, as the UMLS( Metathesaurus( has done for the last six years. Because existing terminologies may overlap--for example, one terminology may name a concept also named by another terminologyQthe terminologies in the Metathesaurus must be merged. Some terminologies suggest merges through their structure or content e.g., they suggest synonyms or connections to other terminologies; other merges can be suggested by algorithm. Regardless, all merges in the Metathesaurus must be approved by a human editor with appropriate domain knowledge. By the time Meta-U96 is released early in 1996, one prototype and seven released versions of the Metathesaurus will have been produced by a sequence of four qualitatively different methods, named for the way in which they merge terms: #1 "Term Rewrite Rules, #2 "Transitive Closure on Facts," #3 "Fact-at-a-Time Concept Merging," and #4 "Action-at-a-Time Object Processing." The development of each method has been constrained by the annual Metathesaurus release schedule. The first two methods made the best use of limited computational resources, and the last two make better use of human editing resources.

National Library of Medicine (U.S.)↗
PubMed1995

Identifying concepts in medical knowledge.

The barrier word method of identifying nominal phrases in text, using a very long barrier word list, was evaluated in two different sets of text. In a sample of 10 paragraphs from the Medical Knowledge Self-Assessment Program of the American College of Physicians, the yield of nominal phrases as a percent of total chunks isolated was 66%. Some 500,000 chunks were isolated from Principles and Practice of Oncology (PPO). 38% of these chunk-occurrences were of chunks which matched to 10,000 concept names in Meta-1.4, the most recent version of the UMLS Metathesaurus. 50 paragraphs from PPO were chosen at random. Co-occurrences of concepts in those paragraphs were reviewed. 42 of the paragraphs had unique or infrequently occurring co-occurrences which described closely the major thrust of the paragraph.

Medical Oncology↗
PubMed1995

Accessing oncology information at the point of care: experience using speech, pen, and 3-D interfaces with a knowledge server.

Oncologists' information needs arise at diverse times and settings. For example: "Is superior vena cava syndrome a medical emergency?" Our collaborative group is developing a system that supports an interface with combinations of spoken, gestural, and simulated three-dimensional manipulation to help an oncologist focus on the information need, not the system. The system requires a small amount of input from the oncologist, and then anticipates what information is pertinent to the patient at hand, based on the sources it has available. The system makes use of a "Knowledge Server" to find relevant information. The Knowledge Server uses selected data for the particular patient from a Computer-based Patient Record (CPR) to provide context for the information needs. The Knowledge Server leverages the Unified Medical Language System (UMLS) resources as well as relevant communications standards. A layered, interaction protocol is used to help manage the fulfillment of information needs. Each of the oncology knowledge sources is transformed into a uniform representation that utilizes both its formal schema (e.g., its table of contents) and its concepts and words indexed through the UMLS Metathesaurus. Our focus on the appropriate use of information from a CPR, and on anticipating oncologists' information needs, resulted from our study of several longitudinal patient scenarios. We believe that our use of scenario-based design techniques will help to ensure the system's success.

California↗
PubMed1994

Formal properties of the Metathesaurus.

The Metathesaurus is a machine-created, human edited and enhanced synthesis of authoritative biomedical terminologies. Its formal properties permit it to be a) exploited by computers, and b) modified and enhanced without compromising that usage. If further constraints were imposed on the existence and identity of Metathesaurus relationships, i.e., if every Metathesaurus concept had a "genus" and a "differentia," then the Metathesaurus could be converted into an "Aristotelian Hierarchy." In this sense, a genus is a concept that classifies another concept, and a differentia is a concept that distinguishes the classified concept from all other concepts in the same class. Since, in principle, these constraints would make the Metathesaurus easier to leverage and maintain computationally, it is interesting to ask to what degree the maintenance and enhancement procedures now in place are producing a Metathesaurus that is also an "Aristotelian Hierarchy." Given a liberal interpretation of the current Metathesaurus schema, the proportion of the Metathesaurus that is "Aristotelian" in each annual version is increasing in spite of dramatic concurrent increases in the number of Metathesaurus concepts. Without formality there is no modifiability nor scalability. [1] We need formal methods and computer-based tools that can help us with the task [of controlled medical vocabulary construction]. We need research in which controlled vocabulary development is the focus rather than a stepping stone for work on other theories and applications. [2]

Subject Headings↗
PubMed1993

Recognizing new medical knowledge computationally.

Can new medical knowledge be recognized computationally? We know knowledge is changing, and our knowledge-based systems will need to accommodate that change in knowledge on a regular basis if they are to stay successful. Computational recognition of these changes seems desirable. It is unlikely that low level objects in the computational universe, bits and characters, will change much over time, higher level objects of language, where meaning begins to emerge, may show change. An analysis of ten arbitrarily selected paragraphs from the Medical Knowledge Self-Assessment Program of the American College of Physicians was used as a test bed for nominal phrase recognition. While there were words not known to Meta-1.2, only 8 of the 32 concepts new to the primary author were pointed to by new words. Use of a barrier word method was successful in identifying 23 of the 32 new concepts. Use of co-occurrence (in sentences) of putative nominal phrases may reduce the amount of human effort involved in recognizing the emergence of new relationships.

Artificial Intelligence↗
PubMed1992

Inhibition of smooth muscle cell proliferation in injured rat arteries. Interaction of heparin with basic fibroblast growth factor.

Heparin inhibits smooth muscle cell (SMC) proliferation after arterial injury by mechanisms that have yet to be defined. Since the initiation of SMC proliferation is mediated by basic fibroblast growth factor (bFGF), we have investigated the possibility that heparin inhibits SMC proliferation by displacing bFGF from the arterial wall. Using a rat carotid artery model of balloon catheter injury, we demonstrate that a bolus injection of heparin depletes the arterial wall of both systemically administered bFGF and of endogenous bFGF. Heparin, however, does not reduce the bFGF content of unmanipulated arteries. Further, a single injection of heparin given at the time of balloon injury reduces SMC proliferation by 55% but has no effect when given 6 h after injury. SMC proliferation induced in a denuded artery by injection of bFGF is inhibited almost completely by a bolus injection of heparin; however, pretreatment with a bolus of heparin does not prevent SMC from responding to a subsequent bolus of bFGF. These experiments suggest that heparin can inhibit SMC proliferation in part by removal of released bFGF from sites of injury.

Animals↗
PubMed1992

The homogenization of the Metathesaurus schema and distribution format.

The third version of the UMLS Metathesaurus, Meta-1.2, to be released in October 1992, will have a simpler schema and simpler distribution formats than the first two versions, Meta-1.0 and Meta-1.1 released in October 1990 and 1991, respectively. For one thing, it will have only a single kind of entry (Concept), rather than three (Concept, Related, and Synonym). Further, the Relational Format, will consist of four logical relations, or tables, instead of the nearly three score different tables used to represent the same kind of information in Meta-1.1. These four tables will contain, respectively, (1) the names of each concept, (2) the relationships between concepts, (3) attributes of the concepts, and (4) a word-based index into the concept names. We argue that the new schema and formats provide a better conceptual model of the Metathesaurus, and represent the information contained there more uniformly. Even though these changes are incremental and evolutionary, both users and software developers should find the Meta-1.2 significantly easier to understand, and the information contained in it significantly easier to use.

Information Storage and Retrieval↗
PubMed1992

The Meta-1.2 engine: a refined strategy for linking biomedical vocabularies.

This paper presents a preliminary description of the database schema and associated procedures that are the foundation for the "engine" that will produce Meta-1.2. Meta-1.2 is the next incarnation of the Metathesaurus, which is one of the principal components of the National Library of Medicine's Unified Medical Language System (UMLS). We use the word "engine" as a generic term that includes a database and the programs that operate on it. While this design builds heavily upon previous work, it incorporates some major changes in philosophy. A major hypothesis is that the simple representation described here is suitable for any controlled vocabulary in the biomedical domain. Indeed, this hypothesis is central to a strategy for producing future versions of the Metathesaurus and for supporting collaboration with people who wish to contribute additional terms and relationships to the Metathesaurus. Another change involves the representation of classes and relationships. The revised database schema includes an explicit representation of the source or "authority" for relationships, which is analogous to the way that the sources of terms have been represented since the first version of the Metathesaurus. A sequence of steps utilizing the new representations to produce the Metathesaurus is presented.

Databases, Factual↗
PubMed1992

The semantic structure of the UMLS Metathesaurus.

Meta-1.1, the UMLS metathesaurus, represents medical knowledge in the forms of names of concepts and links between those concepts. The representations of the semantic neighborhood of a concept can be thought of as dimensions of the property of semantic locality and include term information (broader, narrower, or otherwise related), the contextual information (parent-child, siblings in a hierarchy), the semantic types, and the co-occurrence data (links discovered empirically from concepts used to index the medical literature.) The degree of redundancy of each of these dimensions was investigated by reviewing the extent of multiple presentations of concepts which appear as related to a given concept. The degree of overlap was surprisingly small. While the co-occurrence data finds some of the links represented by other dimensions, those links are but minute fractions of the vast amount of co-occurrence derived links. Because parent-child relationships are often subsumptive (or categorical) in nature, it might be expected that siblings usually share the same semantic types. While true in the aggregate, the wide variance in percent of types shared may reflect the intended usages of the source vocabularies. Noun phrases were extracted from the definitions of 40 concepts in Meta-1 in order to assess systematically the coverage of important concepts by Meta-1, and to assess whether the links between these definitional concepts, which may have special value, and the concept being defined were indeed present. Out of 161 of these definitional concepts, 29 were not represented in Meta-1, and 37 of those represented in Meta-1 had no direct link to the concept they were defining.(ABSTRACT TRUNCATED AT 250 WORDS)

Semantics↗
PubMed1992

Intimal smooth muscle cell proliferation after balloon catheter injury. The role of basic fibroblast growth factor.

The localization and synthesis of basic fibroblast growth factor (bFGF) in the rat carotid artery were investigated at times of chronic smooth muscle cell proliferation. Immunocytochemical staining showed the presence of bFGF in the uninjured arterial wall, and after balloon injury, this cellular staining was decreased. Western and northern blot analyses likewise showed that the amount of bFGF protein and mRNA decreased after injury. A neutralizing antibody to bFGF was administered 4 and 5 days after injury and was found to have no effect on intimal smooth muscle cell proliferation. These data suggest that an increase in the expression of bFGF is not necessary for chronic smooth muscle cell proliferation observed after balloon catheter injury and that bFGF is not the major mitogen responsible for intimal smooth muscle cell proliferation.

Animals↗
PubMed1991

Biomedical database inter-connectivity: an experiment linking MIM, GENBANK, and META-1 via MEDLINE.

The linkage of disparate biomedical databases is an important goal of the Unified Medical Language (UMLS) Project. We conducted an experiment to investigate the feasibility of using UMLS resources to link databases in clinical genetics and molecular biology. References from MIM ("Mendelian Inheritance in Man") were lexically mapped to the equivalent citations in MEDLINE. The MeSH major subject headings by which the citations in a particular MIM entry had been indexed were used to develop a "genetic-disorder-centered view of the world" in Meta-1 (the first official version of the UMLS Metathesaurus). Our hypothesis was that these MeSH subject headings could provide access to a "semantic neighborhood" in Meta-1 that would be relevant to a particular genetic disorder. By browsing in this "semantic neighborhood," a user could select various combinations of terms with which to search MEDLINE through an interface between Meta-1 and Grateful Med. Such searches might retrieve citations that were more recent than those in MIM or that provided useful supplementary information. Since some MEDLINE records contain pointers to entries in GENBANK, information about genetic sequences related to a particular clinical genetic disorder could also be retrieved. This scenario was implemented for a small number of MIM entries, providing a concrete demonstration that linking disparate electronic databases in an important subdomain of biomedicine is relatively straightforward.

Databases, Factual↗
PubMed1991

From meaning to term: semantic locality in the UMLS Metathesaurus.

The Unified Medical Language System Metathesaurus represents the results of a synthesis of existing biomedical naming systems (thesauri). The naming and other information about the meanings in the Metathesaurus can be used to find the preferred naming of that meaning in the source chosen by the user, by exploiting the property of semantic locality. The aspects of semantic locality in the Metathesaurus which can be thus exploited are the terms, the semantic types, the use of that term in a source context, and the co-occurrence of terms in MEDLINE. To find how a meaning is named in the source of choice, a user must exploit one of these aspects of semantic locality, entering a term somehow related to the term being sought, and navigating to the preferred term. While the first three of these aspects of semantic locality are normative, the last is empirical. Testing of the utility of the aspects of semantic locality in information retrieval would require a uniform interface with 1, no Metathesaurus, 2, the Metathesaurus without the aspects in question, and 3, the Metathesaurus including all the aspects. Other potential uses of empirically derived semantic locality include defining or suggesting potentially relevant concepts in a given situation.

Semantics↗
PubMed1991

Adding your terms and relationships to the UMLS Metathesaurus.

The National Library of Medicine's Unified Medical Language System [1] Metathesaurus contains the richest single corpus of biomedical names in existence. Yet, developers wishing to make use of the Metathesaurus will be confronted by users who want to add local terminology and relationships not already represented there. We urge developers to fill those needs, while, at the same time, they plan for the many consequences of unilateral Metathesaurus enhancement. Foremost among these consequences is the need to maintain local enhancements across subsequent releases of the Metathesaurus. These problems are illustrated via examples of candidate Metathesaurus enhancement terms in use at the Columbia-Presbyterian Medical Center (CPMC), at the Mayo Clinic, and in Current Disease Descriptions (CDD). Sharing and reuse of Metathesaurus enhancement methods may permit local enhancements to be used at other sites, and it may permit the global Metathesaurus utilization effort to benefit from economies of scale.

Terminology as Topic↗