Enhanced LexSynonym Acquisition for Effective UMLS Concept Mapping.

Chris J Lu, Destinee Tormey, Lynn McCreedy, Allen C Browne

Abstract read

In one paragraph

Article in Studies in health technology and informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

Using UMLS for electronic health data standardization and database design.Journal of the American Medical Informatics Association : JAMIA · 2020
Article
The Unified Medical Language System SPECIALIST Lexicon and Lexical Tools: Development and applications.Journal of the American Medical Informatics Association : JAMIA · 2020
Article
Normalizing Adverse Events using Recurrent Neural Networks with Attention.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Chris J LuNational Library of Medicine, Bethesda, MD, USA.

Destinee TormeyNational Library of Medicine, Bethesda, MD, USA.

Lynn McCreedyNational Library of Medicine, Bethesda, MD, USA.

Allen C BrowneNational Library of Medicine, Bethesda, MD, USA.

Funding

Intramural NIH HHS Z99 LM999999

6 · The paper itself

Abstract

Concept mapping is important in natural language processing (NLP) for bioinformatics. The UMLS Metathesaurus provides a rich synonym thesaurus and is a popular resource for concept mapping. Query expansion using synonyms for subterm substitutions is an effective technique to increase recall for UMLS concept mapping. Synonyms used to substitute subterms are called element synonyms. The completeness and quality of both element synonyms and the UMLS synonym thesaurus is the key to success in such applications. The Lexical Systems Group (LSG) has developed a new system for element synonym acquisition based on new enhanced requirements and design for better performance. The results show: 1) A 36.71 times growth of synonyms in the Lexicon (lexSynonym) in the 2017 release; 2) Improvements of concept mapping for recall and F1 with similar precision using the lexSynonym.2017 as element synonyms due to the broader coverage and better quality.

Indexed as

Natural Language ProcessingUnified Medical Language SystemSemanticsVocabulary, ControlledNatural Language ProcessingSemanticsUnified Medical Language System

Identifiers

PMID29295145

PMCPMC13470760

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.