Navigating to Knowledge

1995 ◽  
Vol 34 (01/02) ◽  
pp. 214-231 ◽  
Author(s):  
M. S. Tuttle ◽  
W. G. Cole ◽  
D. D. Sherertz ◽  
S. J. Nelson

Abstract: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.

1998 ◽  
Vol 5 (1) ◽  
pp. 76-87 ◽  
Author(s):  
O. Bodenreider ◽  
A. Burgun ◽  
G. Botti ◽  
M. Fieschi ◽  
P. Le Beux ◽  
...  

2020 ◽  
Vol 27 (10) ◽  
pp. 1600-1605 ◽  
Author(s):  
Chris J Lu ◽  
Amanda Payne ◽  
James G Mork

Abstract Natural language processing (NLP) plays a vital role in modern medical informatics. It converts narrative text or unstructured data into knowledge by analyzing and extracting concepts. A comprehensive lexical system is the foundation to the success of NLP applications and an essential component at the beginning of the NLP pipeline. The SPECIALIST Lexicon and Lexical Tools, distributed by the National Library of Medicine as one of the Unified Medical Language System Knowledge Sources, provides an underlying resource for many NLP applications. This article reports recent developments of 3 key components in the Lexicon. The core NLP operation of Unified Medical Language System concept mapping is used to illustrate the importance of these developments. Our objective is to provide generic, broad coverage and a robust lexical system for NLP applications. A novel multiword approach and other planned developments are proposed.


2004 ◽  
Vol 5 (4) ◽  
pp. 354-361 ◽  
Author(s):  
Jane Lomax ◽  
Alexa T. McCray

We have recently mapped the Gene Ontology (GO), developed by the Gene Ontology Consortium, into the National Library of Medicine's Unified Medical Language System (UMLS). GO has been developed for the purpose of annotating gene products in genome databases, and the UMLS has been developed as a framework for integrating large numbers of disparate terminologies, primarily for the purpose of providing better access to biomedical information sources. The mapping of GO to UMLS highlighted issues in both terminology systems. After some initial explorations and discussions between the UMLS and GO teams, the GO was integrated with the UMLS. Overall, a total of 23% of the GO terms either matched directly (3%) or linked (20%) to existing UMLS concepts. All GO terms now have a corresponding, official UMLS concept, and the entire vocabulary is available through the web-based UMLS Knowledge Source Server. The mapping of the Gene Ontology, with its focus on structures, processes and functions at the molecular level, to the existing broad coverage UMLS should contribute to linking the language and practices of clinical medicine to the language and practices of genomics.


2005 ◽  
Vol 6 (1-2) ◽  
pp. 61-66 ◽  
Author(s):  
Karin Verspoor

This paper explores the use of the resources in the National Library of Medicine's Unified Medical Language System (UMLS) for the construction of a lexicon useful for processing texts in the field of molecular biology. A lexicon is constructed from overlapping terms in the UMLS SPECIALIST lexicon and the UMLS Metathesaurus to obtain both morphosyntactic and semantic information for terms, and the coverage of a domain corpus is assessed. Over 77% of tokens in the domain corpus are found in the constructed lexicon, validating the lexicon's coverage of the most frequent terms in the domain and indicating that the constructed lexicon is potentially an important resource for biological text processing.


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