Literature DB >> 32472120

The Unified Medical Language System SPECIALIST Lexicon and Lexical Tools: Development and applications.

Chris J Lu1, Amanda Payne1, James G Mork1.   

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.
© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  NLP tools; lexical tools; lexicon; natural language processing; unified medical language system

Year:  2020        PMID: 32472120     DOI: 10.1093/jamia/ocaa056

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  4 in total

1.  The UMLS knowledge sources at 30: indispensable to current research and applications in biomedical informatics.

Authors:  Betsy L Humphreys; Guilherme Del Fiol; Hua Xu
Journal:  J Am Med Inform Assoc       Date:  2020-10-01       Impact factor: 4.497

2.  Enhancing filter-based parenthetic abbreviation extraction methods.

Authors:  Houcemeddine Turki; Mohamed Ali Hadj Taieb; Mohamed Ben Aouicha
Journal:  J Am Med Inform Assoc       Date:  2021-03-01       Impact factor: 4.497

3.  A deep database of medical abbreviations and acronyms for natural language processing.

Authors:  Lisa Grossman Liu; Raymond H Grossman; Elliot G Mitchell; Chunhua Weng; Karthik Natarajan; George Hripcsak; David K Vawdrey
Journal:  Sci Data       Date:  2021-06-02       Impact factor: 6.444

4.  Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke.

Authors:  Kristiina Rannikmäe; Honghan Wu; Steven Tominey; William Whiteley; Naomi Allen; Cathie Sudlow
Journal:  BMC Med Inform Decis Mak       Date:  2021-06-15       Impact factor: 2.796

  4 in total

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