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.

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

An overview of MetaMap: historical perspective and recent advances

https://www.cs.tufts.edu/comp/150AIH/pdf/NadkarniOC11.pdf

Natural language processing: an introduction.

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.

The Unified Medical Language System.

A simple algorithm for identifying negated findings and diseases in discharge summaries

Access to biomedical terminologies is hampered by the high degree of variability inherent in natural language terms and in the terminologies themselves. The lexicon, lexical programs, databases, and indexes included with the 1994 release of the UMLS Knowledge Sources are designed to help users manage this variability. We describe these resources and illustrate their flexibility and usefulness in providing enhanced access to data in the UMLS Metathesaurus.

Lexical methods for managing variation in biomedical terminologies.

Consumer Health Information Seeking as Hypothesis Testing

Accuracy in information retrieval, that is, achieving both high recall and precision, is challenging because the relationship between natural language and semantic conceptual structure is not straightforward. However, effective retrieval requires that the semantic conceptual structure (or content) of both queries and documents be known. Natural language processing is one way to determine the content of a text. But, due to the complexity involved in natural language processing, various methods have been used which simulate (or approximate) representation of the content of both queries and documents.

Exploiting a large thesaurus for information retrieval

Consumer-friendly Personal Health Records (PHRs) have the potential of providing patients with the basis for taking an active role in their healthcare. However, few studies focused on the features that make health records comprehensible for lay audiences. This paper presents a survey of patients' experience with reviewing their health records, in order to identify barriers to optimal record use. The data are analyzed via descriptive statistical and thematic analysis. The results point to providers' notes, laboratory test results and radiology reports as the most difficult records sections for lay reviewers. Professional medical terminology, lack of explanations of complex concepts (e.g., lab test ranges) and suboptimal data ordering emerge as the most common comprehension barriers. While most patients today access their records in paper format, electronic PHRs present much more opportunities for providing comprehension support.

Towards consumer-friendly PHRs: patients' experience with reviewing their health records.

This paper describes efforts to provide access to the free text in biomedical databases. The focus of the effort is the development of SPECIALIST, an experimental natural language processing system for the biomedical domain. The system includes a broad coverage parser supported by a large lexicon, modules that provide access to the extensive Unified Medical Language System (UMLS) Knowledge Sources, and a retrieval module that permits experiments in information retrieval. The UMLS Metathesaurus and Semantic Network provide a rich source of biomedical concepts and their interrelationships. Investigations have been conducted to determine the type of information required to effect a map between the language of queries and the language of relevant documents. Mappings are never straightforward and often involve multiple inferences.

Allen C. Browne

Papers

Lexical methods for managing variation in biomedical terminologies.

Consumer Health Information Seeking as Hypothesis Testing

Exploiting a large thesaurus for information retrieval

Towards consumer-friendly PHRs: patients' experience with reviewing their health records.

UMLS knowledge for biomedical language processing.