Evidence map›Paper›PMID 23304329›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2012

Towards a semantic lexicon for clinical natural language processing.

Hongfang Liu, Stephen T Wu, Dingcheng Li, Siddhartha Jonnalagadda, Sunghwan Sohn, Kavishwar Wagholikar, Peter J Haug, Stanley M Huff, Christopher G Chute

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. The Sublanguage of Clinical Problem Lists: A Corpus Analysis.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018
    Article
  14. Generalized Extraction and Classification of Span-Level Clinical Phrases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018
    Article
  15. Identifying Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2016
    Article
  16. Article
  17. Facilitating post-surgical complication detection through sublanguage analysis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2014
    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

9 authors.

Hongfang LiuMayo Clinic College of Medicine, Rochester, MN, USA.
Stephen T Wu
Dingcheng Li
Siddhartha Jonnalagadda
Sunghwan Sohn
Kavishwar Wagholikar
Peter J Haug
Stanley M Huff
Christopher G Chute

Funding

Natural language processing for clinical and translational researchR01GM102282 · NIGMS · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, PAKHOMOV, SERGUEI VS · 2013 to 2016
$2.5M
Onto-BioThesaurus: ontological representation of gene/protein names for biomedicaR01LM009959 · NLM · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG · 2009 to 2010
$1.2M
NIGMS NIH HHS R01 GM102282NLM NIH HHS R01 LM009959NLM NIH HHS R01LM009959A1
6 · The paper itself

Abstract

A semantic lexicon which associates words and phrases in text to concepts is critical for extracting and encoding clinical information in free text and therefore achieving semantic interoperability between structured and unstructured data in Electronic Health Records (EHRs). Directly using existing standard terminologies may have limited coverage with respect to concepts and their corresponding mentions in text. In this paper, we analyze how tokens and phrases in a large corpus distribute and how well the UMLS captures the semantics. A corpus-driven semantic lexicon, MedLex, has been constructed where the semantics is based on the UMLS assisted with variants mined and usage information gathered from clinical text. The detailed corpus analysis of tokens, chunks, and concept mentions shows the UMLS is an invaluable source for natural language processing. Increasing the semantic coverage of tokens provides a good foundation in capturing clinical information comprehensively. The study also yields some insights in developing practical NLP systems.

Indexed as

Electronic Health RecordsNatural Language ProcessingUnified Medical Language SystemVocabulary, ControlledDictionaries as TopicHumansSemantics

Identifiers

PMID23304329
PMCPMC3540492

What OpenQuestion holds

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Registered trials

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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.