Clinical acronym/abbreviation normalization using a hybrid approach

Yonghui Wu; Buzhou Tang; Min Jiang; Sungrim Moon; Joshua C. Denny; Hua Xu

Clinical acronym/abbreviation normalization using a hybrid approach

Yonghui Wu
, Buzhou Tang
, Min Jiang
, Sungrim Moon
, Joshua C. Denny
, Hua Xu^*

^*Corresponding author for this work

University of Texas Health Science Center at Houston
Vanderbilt University

Research output: Contribution to journal › Conference article › peer-review

8 Scopus citations

Abstract

A unique characteristic of clinical text is the pervasive use of acronyms and abbreviations, which are often ambiguous. The ShARe/CLEF eHealth Evaluation Lab organized three shared tasks on clinical natural language processing (NLP) and information retrieval (IR) in 2013 and one of them was to normalize acronyms/abbreviations to UMLS concept unique identifiers (CUIs). This paper describes a hybrid system, which combines different Word Sense Disambiguation (WSD) methods and existing knowledge bases to normalize and encode clinical abbreviations. Our system achieved the best accuracy of 0.719 on the independent test set, which was ranked first in the challenge.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	1179
State	Published - 2013
Externally published	Yes
Event	2013 Cross Language Evaluation Forum Conference, CLEF 2013 - Valencia, Spain Duration: 23 Sep 2013 → 26 Sep 2013

Keywords

Clinical abbreviation
Support vector machines
Vector space model
Word sense disambiguation

Cite this

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title = "Clinical acronym/abbreviation normalization using a hybrid approach",

abstract = "A unique characteristic of clinical text is the pervasive use of acronyms and abbreviations, which are often ambiguous. The ShARe/CLEF eHealth Evaluation Lab organized three shared tasks on clinical natural language processing (NLP) and information retrieval (IR) in 2013 and one of them was to normalize acronyms/abbreviations to UMLS concept unique identifiers (CUIs). This paper describes a hybrid system, which combines different Word Sense Disambiguation (WSD) methods and existing knowledge bases to normalize and encode clinical abbreviations. Our system achieved the best accuracy of 0.719 on the independent test set, which was ranked first in the challenge.",

keywords = "Clinical abbreviation, Support vector machines, Vector space model, Word sense disambiguation",

author = "Yonghui Wu and Buzhou Tang and Min Jiang and Sungrim Moon and Denny, \{Joshua C.\} and Hua Xu",

year = "2013",

language = "英语",

volume = "1179",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

note = "2013 Cross Language Evaluation Forum Conference, CLEF 2013 ; Conference date: 23-09-2013 Through 26-09-2013",

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TY - JOUR

T1 - Clinical acronym/abbreviation normalization using a hybrid approach

AU - Wu, Yonghui

AU - Tang, Buzhou

AU - Jiang, Min

AU - Moon, Sungrim

AU - Denny, Joshua C.

AU - Xu, Hua

PY - 2013

Y1 - 2013

N2 - A unique characteristic of clinical text is the pervasive use of acronyms and abbreviations, which are often ambiguous. The ShARe/CLEF eHealth Evaluation Lab organized three shared tasks on clinical natural language processing (NLP) and information retrieval (IR) in 2013 and one of them was to normalize acronyms/abbreviations to UMLS concept unique identifiers (CUIs). This paper describes a hybrid system, which combines different Word Sense Disambiguation (WSD) methods and existing knowledge bases to normalize and encode clinical abbreviations. Our system achieved the best accuracy of 0.719 on the independent test set, which was ranked first in the challenge.

AB - A unique characteristic of clinical text is the pervasive use of acronyms and abbreviations, which are often ambiguous. The ShARe/CLEF eHealth Evaluation Lab organized three shared tasks on clinical natural language processing (NLP) and information retrieval (IR) in 2013 and one of them was to normalize acronyms/abbreviations to UMLS concept unique identifiers (CUIs). This paper describes a hybrid system, which combines different Word Sense Disambiguation (WSD) methods and existing knowledge bases to normalize and encode clinical abbreviations. Our system achieved the best accuracy of 0.719 on the independent test set, which was ranked first in the challenge.

KW - Clinical abbreviation

KW - Support vector machines

KW - Vector space model

KW - Word sense disambiguation

UR - https://www.scopus.com/pages/publications/84922032636

M3 - 会议文章

AN - SCOPUS:84922032636

SN - 1613-0073

VL - 1179

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 2013 Cross Language Evaluation Forum Conference, CLEF 2013

Y2 - 23 September 2013 through 26 September 2013

ER -

Clinical acronym/abbreviation normalization using a hybrid approach

Abstract

Keywords

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