Evidence map›Paper›PMID 17238316›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2006

Disseminating natural language processed clinical narratives.

Elizabeth S Chen, George Hripcsak, Carol Friedman

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Article
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  6. Review
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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

3 authors.

Elizabeth S ChenDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
George Hripcsak
Carol Friedman

Funding

DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M · 2000 to 2023
$10.6M
A Biomedical Natural Language Processing ResourceR01LM008635 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI FRIEDMAN, CAROL · 2005 to 2008
$2.1M
Capturing and linking genomic and clinical informationR01LM007659 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI FRIEDMAN, CAROL · 2003 to 2006
$1.9M
NLM NIH HHS LM006910NLM NIH HHS LM007659NLM NIH HHS LM008635NLM NIH HHS R01 LM006910NLM NIH HHS R01 LM007659NLM NIH HHS R01 LM008635
6 · The paper itself

Abstract

Through Natural Language Processing (NLP) techniques, information can be extracted from clinical narratives for a variety of applications (e.g., patient management). While the complex and nested output of NLP systems can be expressed in standard formats, such as the eXtensible Markup Language (XML), these representations may not be directly suitable for certain end-users or applications. The availability of a âeuro tabular' format that simplifies the content and structure of NLP output may facilitate the dissemination and use by users who are more familiar with common spreadsheet, database, or statistical tools. In this paper, we describe the knowledge-based design of a tabular representation for NLP output and development of a transformation program for the structured output of MedLEE, an NLP system at our institution. Through an evaluation, we found that the simplified tabular format is comparable to existing more complex NLP formats in effectiveness for identifying clinical conditions in narrative reports.

Indexed as

Medical Records Systems, ComputerizedNatural Language ProcessingProgramming LanguagesForms and Records ControlHumansInformation Storage and RetrievalKnowledge Bases

Identifiers

PMID17238316
PMCPMC1839529

What OpenQuestion holds

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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.