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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↗

PubMed2005

Integrating SNOMED CT into the UMLS: an exploration of different views of synonymy and quality of editing.

OBJECTIVE: The integration of SNOMED CT into the Unified Medical Language System (UMLS) involved the alignment of two views of synonymy that were different because the two vocabulary systems have different intended purposes and editing principles. The UMLS is organized according to one view of synonymy, but its structure also represents all the individual views of synonymy present in its source vocabularies. Despite progress in knowledge-based automation of development and maintenance of vocabularies, manual curation is still the main method of determining synonymy. The aim of this study was to investigate the quality of human judgment of synonymy. DESIGN: Sixty pairs of potentially controversial SNOMED CT synonyms were reviewed by 11 domain vocabulary experts (six UMLS editors and five noneditors), and scores were assigned according to the degree of synonymy. MEASUREMENTS: The synonymy scores of each subject were compared to the gold standard (the overall mean synonymy score of all subjects) to assess accuracy. Agreement between UMLS editors and noneditors was measured by comparing the mean synonymy scores of editors to noneditors. RESULTS: Average accuracy was 71% for UMLS editors and 75% for noneditors (difference not statistically significant). Mean scores of editors and noneditors showed significant positive correlation (Spearman's rank correlation coefficient 0.654, two-tailed p < 0.01) with a concurrence rate of 75% and an interrater agreement kappa of 0.43. CONCLUSION: The accuracy in the judgment of synonymy was comparable for UMLS editors and nonediting domain experts. There was reasonable agreement between the two groups.

Semantics↗

PubMed1994

Using POSTDOC to recognize biomedical concepts in medical school curricular documents.

Recognition of the biomedical concepts in a document is prerequisite to further processing of the document: medical educators examine curricular documents to discover the coverage of certain topics, detect unwanted redundancies, integrate new content, and delete old content; and clinicians are concerned with terms in patient medical records for purposes ranging from creation of an electronic medical record to identification of medical literature relevant to a particular case. POSTDOC (POSTprocessor of DOCuments) is a computer application that (1) accepts as input a free-text, ASCII-formatted document and uses the Unified Medical Language System (UMLS) Metathesaurus to recognize relevant main concept terms; (2) provides term co-occurrence data and thus is able to identify potentially increasing correlations among concepts within the document; and (3) retrieves references from MEDLINE files based on user identification of relevant subjects. This paper describes a formative evaluation of POSTDOC's ability to recognize UMLS Metathesaurus biomedical concepts in medical school lecture outlines. The "precision" and "recall" varied over a wide range and were deemed not yet acceptable for automated creation of a database of concepts from curricular documents. However, results were good enough to warrant further study and continued system development.

Curriculum↗

PubMed2004

Knowledge sharing and information integration in healthcare using ontologies and deductive databases.

This paper describes a method for using Semantic Web technologies for sharing knowledge in healthcare. It combines deductive databases and ontologies, so that it is possible to extract knowledge that has not been explicitly declared within the database. A representation of the UMLS (Unified Medical Language System) Semantic Network and Metathesaurus was created using the RDF standard, in order to represent the basic medical ontology. The inference over the knowledge base is done by the TRI-DEDALO System, a deductive data-base created to query and update RDF based knowledge sources as well as conventional relational databases. Finally, an ontology was created for the Brazilian National Health Card data interchange format, a standard to capture and transmit health encounter information throughout the country. This paper demonstrates how this approach can be used to integrate heterogeneous information and to answer complex queries in a real world environment.

Brazil↗

PubMed1998

UMLS-based access to CPR data.

This paper describes the results of a project that explores the use the Unified Medical Language System (UMLS) for knowledge-driven tasks, such as browsing a computer-based patient record (CPR). The project consisted of a number of steps: the mapping between CPR terms and UMLS concepts, the development of an algorithm that explores the CPR data using this mapping, and the implementation of a first prototype browser that visualizes "found" data. A second task addressed in this project has been the direct access to online medical literature (MEDLINE) using the UMLS concepts found in the CPR data. In this project, we used a preliminary version of the Open Records for Patient Care (ORCA) CPR that consisted only of the history and physical examination data of patient suffering from heart failure.

Algorithms↗

PubMed1995

ASN.1: defining a grammar for the UMLS knowledge sources.

The unified Medical Language System (UMLS) project provides resources on an experimental basis to the research community. In 1995 the four UMLS Knowledge Sources have been provided in an additional data format, Abstract Syntax Notation One (ASN.1). The benefits of ASN.1 are that it provides a standard, formal grammar for complex data and allows exchange of that data in a way which is independent of the particular software and hardware environment in which the data are created and stored. The paper begins with an introduction to the ASN.1 standard itself. It continues with a discussion of the ASN.1 implementation of the UMLS Knowledge Sources and some of the consequences for the newly released UMLS Knowledge Source Server. It concludes with a discussion of some of the benefits of using ASN.1 encoded data.

Computer Communication Networks↗

PubMed2004

Pathology abbreviated: a long review of short terms.

CONTEXT: Abbreviations are used frequently in pathology reports and medical records. Efforts to identify and organize free-text concepts must correctly interpret medical abbreviations. During the past decade, the author has collected more than 12 000 medical abbreviations, concentrating on terms used or interpreted by pathologists. OBJECTIVE: The purpose of the study is to provide readers with a listing of abbreviations. The listing of abbreviations is reviewed for the purpose of determining the variety of ways that long forms are shortened. DESIGN: Abbreviations fell into different classes. These classes seemed amenable to distinct algorithmic approaches to their correct expansions. A discussion of these abbreviation classes was included to assist informaticians who are searching for ways to write software that expands abbreviations found in medical text. Classes were separated by the algorithmic approaches that could be used to map abbreviations to their correct expansions. A Perl implementation was developed to automatically match expansions with Unified Medical Language System concepts. MEASUREMENTS: The abbreviation list contained 12 097 terms; 5772 abbreviations had unique expansions. There were 6325 polysemous abbreviation/expansion pairs. The expansions of 8599 abbreviations mapped to Unified Medical Language System concepts. Three hundred twenty-four abbreviations could be confused with unabbreviated words. Two hundred thirteen abbreviations had different expansions depending on whether the American or the British spellings were used. Nine hundred seventy abbreviations ended in the letter "s."Results.-There were 6 nonexclusive groups of abbreviations classed by expansion algorithm, as follows: (1) ephemeral; (2) hyponymous; (3) monosemous; (4) polysemous; (5) masqueraders of common words; and (6) fatal (abbreviations whose incorrect expansions could easily result in clinical errors). CONCLUSION: Collecting and classifying abbreviations creates a logical approach to the development of class-specific algorithms designed to expand abbreviations. A large listing of medical abbreviations is placed into the public domain. The most current version is available at http://www.pathologyinformatics.org/downloads/abbtwo.htm.

Abbreviations as Topic↗

PubMed2005

Mapping cancer patients' symptoms to UMLS concepts.

A prerequisite for patient-friendly personal health records (PHR) is their ability to allow seamless integration of patient terminology with professional terminologies. In this work, we mapped a set of symptoms/ problems from the self-assessment component of a cancer patient support system to concepts in the Unified Medical Language System (UMLS) Metathesaurus. Our objective was to learn how the UMLS can be used as a tool to connect patient terminology with professional vocabularies. The mapping to UMLS was done with the help of ten expert cancer nurses who evaluated concepts, their synonyms and placement in the source vocabulary hierarchical structure. The UMLS concepts were also compared with terms and phrases found in patient medical records that addressed the same set of symptoms. In this study we observed several problems related to the use of the UMLS Metathesaurus as a tool to connect from patient-level expressions to professional-level classification systems. More work is needed to increase interoperability between layperson health applications and clinical systems.

Feasibility Studies↗

PubMedDate not supplied

The basis for using the Internet to support the information needs of primary care.

Synthesizing the state of the art from the published literature, this review assesses the basis for employing the Internet to support the information needs of primary care. The authors survey what has been published about the information needs of clinical practice, including primary care, and discuss currently available information resources potentially relevant to primary care. Potential methods of linking information needs with appropriate information resources are described in the context of previous classifications of clinical information needs. Also described is the role that existing terminology mapping systems, such as the National Library of Medicine's Unified Medical Language System, may play in representing and linking information needs to answers.

Academic Medical Centers↗

PubMed1994

Users conceptual views on medical information databases.

As information databases we consider all the kinds of information repositories that are handled by computer systems. When querying very large information databases, the end-users are often faced with the problem to parse their questions efficiently into the query languages of the computer systems. Conceptual graphs were initially designed for natural language analysis and understanding. Due to their closeness to semantic networks, their expressiveness is powerful enough to be applied to knowledge representation and use by computer systems. This work demonstrates that conceptual graphs are a suitable means to model both the information in patient databases and the queries to these databases, and that operations on graphs can compute the pattern matching process needed to provide the answers. A prototype that exploits this model is presented. Experiments have been made with the material furnished by the Unified Medical Language System project (version 2, 1992) of the National Library of Medicine, USA.

Abstracting and Indexing↗

PubMed1991

An automatic indexing method for medical documents.

This paper describes MetaIndex, an automatic indexing program that creates symbolic representations of documents for the purpose of document retrieval. MetaIndex uses a simple transition network parser to recognize a language that is derived from the set of main concepts in the Unified Medical Language System Metathesaurus (Meta-1). MetaIndex uses a hierarchy of medical concepts, also derived from Meta-1, to represent the content of documents. The goal of this approach is to improve document retrieval performance by better representation of documents. An evaluation method is described, and the performance of MetaIndex on the task of indexing the Slice of Life medical image collection is reported.

Abstracting and Indexing↗

PubMed1995

Free text analysis.

In the context of hospital information systems (HIS) medical free text analysis is reviewed with respect to current automated approaches to literature retrieval, case retrieval and fact retrieval from textual data in the patient record. The Unified Medical Language System (UMLS) project has enormously stimulated current research. It is expected that UMLS knowledge sources and SNOMED III (which need a translation into other languages as soon as possible) as well as the conceptual graphs formalism, could become standards to utilize free text information contained in HIS databases.

Abstracting and Indexing↗

PubMed1993

How useful is the UMLS metathesaurus in developing a controlled vocabulary for an automated problem list?

We are developing a set of problem list phrases to be used in the automated problem list of a prototype clinical computing system. Because of the large number of terms in the Unified Medical Language System (UMLS) and the links between them, we are experimenting with the use of the UMLS as the foundation for our problem list phrase set. We have found the UMLS to be very useful for this project, but that it lacks many phases clinicians wish to include in the problem list. Internal linkages between phrases provided in the UMLS are not well suited to our needs. We plan to continue our use of the UMLS but to add problem list phrases and linkages between phrases to support browsing and decision support applications.

Ambulatory Care Information Systems↗

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↗

PubMed2003

Modular representation of the guideline text: an approach for maintaining and updating the content of medical education.

One of the principal challenges in the medical practice is the update of their knowledge. One of the prime roles of the Continuing Medical Education is to train the medical practitioners with the latest advances in health care, specialized to their needs. Online courses and classroom teaching with computer-based representations have become an established mode of delivering medical education. This paper deals with the modularized representation of a medical text concerning clinical practice guidelines. The proposed system takes into consideration the semantics of the Unified Medical Language System and is based upon the marking up and display of the knowledge using the XML and XSLT languages. This modularization of the concepts leads to the determination of the context of a portion or the whole document. Thus, after marking up using our system, the text components can be exchanged, modified or reconstructed, which, in turn, would help to maintain the updates in medical knowledge.

Artificial Intelligence↗

PubMed1993

Using a network menu and the UMLS Information Sources Map to facilitate access to online reference materials.

As computer technology advances, clinicians and biomedical researchers are becoming more dependent upon information from online databases and information systems. By using specially configured computer workstations and high-speed computer networks, it is now possible to access this information in a rapid and straightforward manner. To empower users by providing these capabilities, the authors are assembling a variety of network workstations to be located throughout Yale-New Haven Medical Center. At the heart of the workstation is NetMenu, a program designed to help users connect to a number of important online information systems, including a hospital order entry and results reporting system, a drug reference, bibliographic retrieval systems, and educational programs. In addition, as part of the National Library of Medicine's Unified Medical Language System (UMLS) project, the authors have developed a local prototype of the UMLS Information Sources Map (ISM) and a companion query assistant program to complement the NetMenu in helping users select and connect automatically to information services relevant to a particular question. The ISM query assistant draws from a listing of many online information sources accessible via local and international networks.

Computer Communication Networks↗

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↗

PubMed1995

Navigating to knowledge.

One way to fulfill point-of-care knowledge needs is to present caregivers with a visual representation of the available "answers". Using such a representation, caregivers can recognize what they want, rather than have to recall what they need, and then navigate to an appropriate answer. Given selected pieces of information from a computer-based patient record, an interface can anticipate certain knowledge needs by initializing caregiver navigation in a semantic neighborhood of answers likely to be relevant to the patient at hand. These notions draw heavily on two collaborative projects--the U.S. National Library of Medicine Unified Medical Language System and the U.S. National Cancer Institute Knowledge Server. Both of these projects support navigation because they make the structure of medical knowledge explicit in a way that can be exploited by human interfaces.

Artificial Intelligence↗