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The Unified Medical Language System.

In 1986, the National Library of Medicine began a long-term research and development project to build the Unified Medical Language System (UMLS). The purpose of the UMLS is to improve the ability of computer programs to "understand" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users. Underlying the UMLS effort is the assumption that timely access to accurate and up-to-date information will improve decision making and ultimately the quality of patient care and research. The development of the UMLS is a distributed national experiment with a strong element of international collaboration. The general strategy is to develop UMLS components through a series of successive approximations of the capabilities ultimately desired. Three experimental Knowledge Sources, the Metathesaurus, the Semantic Network, and the Information Sources Map have been developed and are distributed annually to interested researchers, many of whom have tested and evaluated them in a range of applications. The UMLS project and current developments in high-speed, high-capacity international networks are converging in ways that have great potential for enhancing access to biomedical information.

Information Storage and Retrieval↗

UMLS-based conceptual queries to biomedical information databases: an overview of the project ARIANE. Unified Medical Language System.

OBJECTIVE: The aim of the project ARIANE is to model and implement seamless, natural, and easy-to-use interfaces with various kinds of heterogeneous biomedical information databases. DESIGN: A conceptual model of some of the Unified Medical Language System (UMLS) knowledge sources has been developed to help end users to query information databases. A query is represented by a conceptual graph that translates the deep structure of an end-user's interest in a topic. A computational model exploits this conceptual model to build a query interactively represented as query graph. A query graph is then matched to the data graph built with data issued from each record of a database by means of a pattern-matching (projection) rule that applies to conceptual graphs. RESULTS: Prototypes have been implemented to test the feasibility of the model with different kinds of information databases. Three cases are studied: 1) information in records is structured according to the UMLS knowledge sources; 2) information is able to be structured without error in the frame of the UMLS knowledge; 3) information cannot be structured. In each case the pattern-matching is processed by the projection rule according to the structure of information that has been implemented in the databases. CONCLUSION: The conceptual graphs theory provides with a homogeneous and powerful formalism able to represent both concepts, instances of concepts in medical contexts, and associations by means of relationships, and to represent data at different levels of details. The conceptual-graphs formalism allows powerful capabilities to operate a semantic integration of information databases using the UMLS knowledge sources.

Databases as Topic↗

A tool for sharing annotated research data: the "Category 0" UMLS (Unified Medical Language System) vocabularies.

BACKGROUND: Large biomedical data sets have become increasingly important resources for medical researchers. Modern biomedical data sets are annotated with standard terms to describe the data and to support data linking between databases. The largest curated listing of biomedical terms is the the National Library of Medicine's Unified Medical Language System (UMLS). The UMLS contains more than 2 million biomedical terms collected from nearly 100 medical vocabularies. Many of the vocabularies contained in the UMLS carry restrictions on their use, making it impossible to share or distribute UMLS-annotated research data. However, a subset of the UMLS vocabularies, designated Category 0 by UMLS, can be used to annotate and share data sets without violating the UMLS License Agreement. METHODS: The UMLS Category 0 vocabularies can be extracted from the parent UMLS metathesaurus using a Perl script supplied with this article. There are 43 Category 0 vocabularies that can be used freely for research purposes without violating the UMLS License Agreement. Among the Category 0 vocabularies are: MESH (Medical Subject Headings), NCBI (National Center for Bioinformatics) Taxonomy and ICD-9-CM (International Classification of Diseases-9-Clinical Modifiers). RESULTS: The extraction file containing all Category 0 terms and concepts is 72,581,138 bytes in length and contains 1,029,161 terms. The UMLS Metathesaurus MRCON file (January, 2003) is 151,048,493 bytes in length and contains 2,146,899 terms. Therefore the Category 0 vocabularies, in aggregate, are about half the size of the UMLS metathesaurus.A large publicly available listing of 567,921 different medical phrases were automatically coded using the full UMLS metatathesaurus and the Category 0 vocabularies. There were 545,321 phrases with one or more matches against UMLS terms while 468,785 phrases had one or more matches against the Category 0 terms. This indicates that when the two vocabularies are evaluated by their fitness to find at least one term for a medical phrase, the Category 0 vocabularies performed 86% as well as the complete UMLS metathesaurus. CONCLUSION: The Category 0 vocabularies of UMLS constitute a large nomenclature that can be used by biomedical researchers to annotate biomedical data. These annotated data sets can be distributed for research purposes without violating the UMLS License Agreement. These vocabularies may be of particular importance for sharing heterogeneous data from diverse biomedical data sets. The software tools to extract the Category 0 vocabularies are freely available Perl scripts entered into the public domain and distributed with this article.

Algorithms↗

Knowledge representation and indexing using the unified medical language system.

Ontologies and semantic frameworks can be used to improve the accuracy and expressiveness of natural language processing for the purpose of extracting meaning from technical documents. This is especially true when a rich ontology such as the Unified Medical Language System (UMLS) is available. This paper reports on some tools being developed to make this possible and on some experience with a user interface based on ontologies and semantic networks that allows for interactive knowledge exploration.

Abstracting and Indexing↗

Unified medical language system coverage of emergency-medicine chief complaints.

BACKGROUND: Emergency department (ED) chief-complaint (CC) data increasingly are important for clinical-care and secondary uses such as syndromic surveillance. There is no widely used ED CC vocabulary, but experts have suggested evaluation of existing health-care vocabularies for ED CC. OBJECTIVES: To evaluate the ED CC coverage in existing biomedical vocabularies from the Unified Medical Language System (UMLS). METHODS: The study sample included all CC entries for all visits to three EDs over one year. The authors used a special-purpose text processor to clean CC entries, which then were mapped to UMLS concepts. The UMLS match rates then were calculated and analyzed for matching concepts and nonmatching entries. RESULTS: A total of 203,509 ED visits was included. After cleaning with the text processor, 82% of the CCs matched a UMLS concept. The authors identified 5,617 unique UMLS concepts in the ED CC data, but many were used for only one or two visits. One thousand one hundred thirty-six CC concepts were used more than ten times and covered 99% of all the ED visits. The largest biomedical vocabulary in the UMLS is the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), which included concepts for 79% of all ED CC entries. However, some common CCs were not found in SNOMED CT. CONCLUSIONS: The authors found that ED CC concepts are well covered by the UMLS and that the best source of vocabulary coverage is from SNOMED CT. There are some gaps in UMLS and SNOMED CT coverage of ED CCs. Future work on vocabulary control for ED CCs should build upon existing vocabularies.

Data Collection↗

A schematic analysis of the Unified Medical Language System.

The UMLS is a complex collection of medical terms and relationships derived from standard classifications. Appreciating the scope and layout of these relations from text descriptions of relational schema is difficult. The graphical technique of Logical Data Structure (LDS) representation was employed to illustrate the UMLS schema as a data abstraction, affording additional insights that might otherwise escape notice. An LDS representation of the Metathesaurus offers the following advantages: 1) the separation of a viewpoint from physical data structures enables a global outline of the contents; 2) the graphical map makes the interrelation of data visible; and 3) the logical entities explicitly reflect the decision-making which was implicit or ambiguous in the relational scheme.

Models, Theoretical↗

Standardized problem list generation, utilizing the Mayo canonical vocabulary embedded within the Unified Medical Language System.

UNLABELLED: VOCABULARY: The Mayo problem list vocabulary is a clinically derived lexicon created from the entries made to the Mayo Clinic's Master Sheet Index and the problem list entries made to the Impression/ Report/Plan section of the Clinical Notes System over the last three years. The vocabulary was reduced by eliminating repetition including lexical variants, spelling errors, and qualifiers (Administrative or Operational terms). Qualifiers are re-coordinated with other terms, at run-time, which greatly increased the number of input strings which our system is capable of recognizing. IMPLEMENTATION: The Problem Manager is implemented using standard windows tools in a Windows NT environment. The interface is designed using Object Pascal. HTTP calls are passed over the World Wide Web to a UNIX based vocabulary server. The server returns a document, which is read into Object Pascal structures, parsed, filtered and displayed. STUDY: This paper reports the results of a recent Usability Trial focused on assessing the viability of this mechanism for standardized problem entry. Eight clinicians engaged in eleven scenarios and responded as to their satisfaction with the systems performance. These responses were observed, videotaped and tabulated. Clinicians in this study were able to find acceptable diagnoses in 91.1% of the scenarios. The response time was acceptable in 92.5% of the scenarios. The presentation of related terms was stated to be useful in at least one scenario by seven of the eight participants. All clinicians wanted to make use of shortcuts which would minimize the amount of typing necessary to encode the concept they were searching for (e.g. Abbreviations, Word Completion). CONCLUSIONS: Clinicians are willing to choose a canonical term from a suggested list (as opposed to their own wording). Clinicians want an "intelligent" system, which would suggest terms within a category (e.g. Types of "Migraine"). They are able to make functional use of our system, in its current state of development. Finally, all clinicians appreciate the value of encoding their problems in a standardized vocabulary, toward improved research, education and practice.

Computer Communication Networks↗

A study of abbreviations in the UMLS.

Abbreviations are widely used in medicine. The understanding of abbreviations is important for medical language processing and information retrieval systems. The Unified Medical Language System (UMLS) contains a large number of abbreviations. We hypothesized that extracting and studying the UMLS abbreviations can be helpful for understanding the characteristics of abbreviations in medicine. In this paper, we describe a method for extracting abbreviations from the UMLS. We evaluated the method and studied the ambiguous nature of the abbreviations. In addition, the coverage of the UMLS abbreviations in medical reports was studied. Using our method, we extracted 163,666 unique (abbreviation, full form) pairs from the UMLS with a precision of 97.5%, and a recall of 96%. The UMLS abbreviations were highly ambiguous: 33.1% of abbreviations with six characters or less had multiple meanings; the average number of different full forms for all abbreviations with six characters or less was 2.28. The coverage of the UMLS abbreviations in medical reports was over 66%.

Abbreviations as Topic↗

Empirical derivation of an electronic clinically useful problem statement system.

Problem lists are tools to improve patient management. In the medical record, they connect diagnoses to therapy, prognosis, and psychosocial issues. Computer-based problem lists enhance paper-based approaches by enabling cost-containment and quality assurance applications, but they require clinically expressive controlled vocabularies. Because existing controlled vocabularies do not represent problem statements at a clinically useful level, we derived a new canonical problem statement vocabulary through semi-automated analysis and distillation of provider-entered problem lists collected over 6 years from 74,696 patients. We combined automated and manual methods to condense 891,770 problem statements entered by 1961 care providers at Grady Memorial Hospital in Atlanta, Georgia, to 15,534 Canonical Clinical Problem Statement System (CCPSS) terms. The nature and frequency of problem statements were characterized, interrelations among them were enumerated, and a database capturing the epidemiology of problems was created. The authors identified 23,503 problem relations (co-occurrences, sign-symptom complexes, and differential diagnoses) and 22,690 modifier words that further categorized "canonical" problems. To assess completeness, CCPSS content was compared with that of the 1997 Unified Medical Language System Metathesaurus (containing terms from 44 clinical vocabularies). Unified Medical Language System terms expressed 25% of individual CCPSS terms exactly (71% of problems by frequency), 27% partially, and 48% poorly or not at all. Clinicians judged that CCPSS terms completely captured their clinical intent for 84% of 686 randomly selected free-text problem statements. The CCPSS represents clinical concepts at a level exceeding that of previous approaches. A similar national approach could create a standardized, useful, shared resource for clinical practice.

Humans↗

Aggregating UMLS semantic types for reducing conceptual complexity.

The conceptual complexity of a domain can make it difficult for users of information systems to comprehend and interact with the knowledge embedded in those systems. The Unified Medical Language System (UMLS) currently integrates over 730,000 biomedical concepts from more than fifty biomedical vocabularies. The UMLS semantic network reduces the complexity of this construct by grouping concepts according to the semantic types that have been assigned to them. For certain purposes, however, an even smaller and coarser-grained set of semantic type groupings may be desirable. In this paper, we discuss our approach to creating such a set. We present six basic principles, and then apply those principles in aggregating the existing 134 semantic types into a set of 15 groupings. We present some of the difficulties we encountered and the consequences of the decisions we have made. We discuss some possible uses of the semantic groups, and we conclude with implications for future work.

Semantics↗

Concept-value pair extraction from semi-structured clinical narrative: a case study using echocardiogram reports.

The task of gathering detailed patient information from narrative text presents a significant barrier to clinical research. A prototype information extraction system was developed to identify concepts and their associated values from narrative echocardiogram reports. The system uses a Unified Medical Language System compatible architecture and takes advantage of canonical language use patterns to identify sentence templates with which concepts and their related values can be identified. The data extracted from this system will be used to enrich an existing database used by clinical researchers in a large university healthcare system to identify potential research candidates fulfilling clinical inclusion criteria. The system was developed and evaluated using ten clinical concepts. Concept-value pairs extracted by the system were compared with findings extracted manually by the author. The system was able to recall 78% [95%CI, 76-80%] of the relevant findings, with a precision of 99% [95%CI, 98-99%].

Echocardiography↗

Resources for comparing the speed and performance of medical autocoders.

BACKGROUND: Concept indexing is a popular method for characterizing medical text, and is one of the most important early steps in many data mining efforts. Concept indexing differs from simple word or phrase indexing because concepts are typically represented by a nomenclature code that binds a medical concept to all equivalent representations. A concept search on the term renal cell carcinoma would be expected to find occurrences of hypernephroma, and renal carcinoma (concept equivalents). The purpose of this study is to provide freely available resources to compare speed and performance among different autocoders. These tools consist of: 1) a public domain autocoder written in Perl (a free and open source programming language that installs on any operating system); 2) a nomenclature database derived from the unencumbered subset of the publicly available Unified Medical Language System; 3) a large corpus of autocoded output derived from a publicly available medical text. METHODS: A simple lexical autocoder was written that parses plain-text into a listing of all 1,2,3, and 4-word strings contained in text, assigning a nomenclature code for text strings that match terms in the nomenclature. The nomenclature used is the unencumbered subset of the 2003 Unified Medical Language System (UMLS). The unencumbered subset of UMLS was reduced to exclude homonymous one-word terms and proper names, resulting in a term/code data dictionary containing about a half million medical terms. The Online Mendelian Inheritance in Man (OMIM), a 92+ Megabyte publicly available medical opus, was used as sample medical text for the autocoder. RESULTS: The autocoding Perl script is remarkably short, consisting of just 38 command lines. The 92+ Megabyte OMIM file was completely autocoded in 869 seconds on a 2.4 GHz processor (less than 10 seconds per Megabyte of text). The autocoded output file (9,540,442 bytes) contains 367,963 coded terms from OMIM and is distributed with this manuscript. CONCLUSIONS: A public domain Perl script is provided that can parse through plain-text files of any length, matching concepts against an external nomenclature. The script and associated files can be used freely to compare the speed and performance of autocoding software.

Abstracting and Indexing↗

Prototyping an institutional IAIMS/UMLS information environment for an academic medical center.

The paper describes a prototype information environment designed to link network-based information resources in an integrated fashion and thus enhance the information capabilities of an academic medical center. The prototype was implemented on a single Macintosh computer to permit exploration of the overall "information architecture" and to demonstrate the various desired capabilities prior to full-scale network-based implementation. At the heart of the prototype are two components: a diverse set of information resources available over an institutional computer network and an information sources map designed to assist users in finding and accessing information resources relevant to their needs. The paper describes these and other components of the prototype and presents a scenario illustrating its use. The prototype illustrates the link between the goals of two National Library of Medicine initiatives, the Integrated Academic Information Management System (IAIMS) and the Unified Medical Language System (UMLS).

Academic Medical Centers↗

An information sources map for Occupational and Environmental Medicine: guidance to network-based information through domain-specific indexing.

This paper describes a prototype information sources map (ISM), an on-line information source finder, for Occupational and Environmental Medicine (OEM). The OEM ISM was built as part of the Unified Medical Language System (UMLS) project of the National Library of Medicine. It allows a user to identify sources of on-line information appropriate to a specific OEM question, and connect to the sources. In the OEM ISM we explore a domain-specific method of indexing information source contents, and also a domain-specific user interface. The indexing represents a domain expert's opinion of the specificity of an information source in helping to answer specific types of domain questions. For each information source, an index field represents whether a source might provide useful information in an occupational, industrial, or environmental category. Additional fields represent the degree of specificity of a source in individual question types in each category. The paper discusses the development, design, and implementation of the prototype OEM ISM.

Abstracting and Indexing↗