Evidence map›Paper›PMID 24943582›Full record

ArticleStudies in health technology and informatics2014

Towards symbiosis in knowledge representation and natural language processing for structuring clinical practice guidelines.

Chunhua Weng, Philip R O Payne, Mark Velez, Stephen B Johnson, Suzanne Bakken

Abstract read
In one paragraph

Article in Studies in health technology and informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Chunhua WengDepartment of Biomedical Informatics, Columbia University, New York, New York.
Philip R O PayneDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH.
Mark VelezDepartment of Biomedical Informatics, Columbia University, New York, New York.
Stephen B JohnsonDepartment of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York.
Suzanne BakkenSchool of Nursing, Columbia University, New York, NY.

Funding

Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical DataR01LM009886 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WENG, CHUNHUA · 2009 to 2020
$5.3M
Developing Flexible EHR Plug-ins to Re-Engineer Clinical Care and Research WorkflowsR01LM010815 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HOMMA, SHUNICHI, WENG, CHUNHUA · 2010 to 2013
$1.9M
WICER 4UR01HS022961 · AHRQ · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BAKKEN, SUZANNE · 2013 to 2013
$750k
AHRQ HHS R01 HS022961AHRQ HHS R01HS022961NCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1TR000040NLM NIH HHS R01 LM009886NLM NIH HHS R01LM009886NLM NIH HHS R01 LM010815NLM NIH HHS R01LM010815
6 · The paper itself

Abstract

The successful adoption by clinicians of evidence-based clinical practice guidelines (CPGs) contained in clinical information systems requires efficient translation of free-text guidelines into computable formats. Natural language processing (NLP) has the potential to improve the efficiency of such translation. However, it is laborious to develop NLP to structure free-text CPGs using existing formal knowledge representations (KR). In response to this challenge, this vision paper discusses the value and feasibility of supporting symbiosis in text-based knowledge acquisition (KA) and KR. We compare two ontologies: (1) an ontology manually created by domain experts for CPG eligibility criteria and (2) an upper-level ontology derived from a semantic pattern-based approach for automatic KA from CPG eligibility criteria text. Then we discuss the strengths and limitations of interweaving KA and NLP for KR purposes and important considerations for achieving the symbiosis of KR and NLP for structuring CPGs to achieve evidence-based clinical practice.

Indexed as

Database Management SystemsKnowledge BasesNatural Language ProcessingPractice Guidelines as TopicVocabulary, ControlledDatabases, FactualInformation Storage and RetrievalNew YorkSemantics

Identifiers

PMID24943582
PMCPMC4445724

What OpenQuestion holds

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LicenceCC BY-NC
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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.