Evidence map›Paper›PMID 28163196›Full record

ArticleJournal of biomedical informatics2017

Extraction of left ventricular ejection fraction information from various types of clinical reports.

Youngjun Kim, Jennifer H Garvin, Mary K Goldstein, Tammy S Hwang, Andrew Redd, Dan Bolton, Paul A Heidenreich, Stéphane M Meystre

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Review
  3. ViViEchoformer: Deep Video Regressor Predicting Ejection Fraction.Journal of imaging informatics in medicine · 2025
    Article
  4. Article
  5. ViViEchoformer: Deep Video Regressor Predicting Ejection Fraction.medRxiv : the preprint server for health sciences · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Observational
  11. Article
  12. From Sour Grapes to Low-Hanging Fruit: A Case Study Demonstrating a Practical Strategy for Natural Language Processing Portability.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018
    Article
  13. Observational
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

8 authors.

Youngjun KimSchool of Computing, University of Utah, Salt Lake City, UT, USA; VA Health Care System, Salt Lake City, UT, USA. Electronic address: youngjun@cs.utah.edu.
Jennifer H GarvinVA Health Care System, Salt Lake City, UT, USA; Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
Mary K GoldsteinVA Palo Alto Health Care System, Palo Alto, CA, USA; Stanford University, Stanford, CA, USA.
Tammy S HwangVA Palo Alto Health Care System, Palo Alto, CA, USA.
Andrew ReddVA Health Care System, Salt Lake City, UT, USA; Division of Epidemiology, University of Utah, Salt Lake City, UT, USA.
Dan BoltonVA Health Care System, Salt Lake City, UT, USA; Division of Epidemiology, University of Utah, Salt Lake City, UT, USA.
Paul A HeidenreichVA Palo Alto Health Care System, Palo Alto, CA, USA; Stanford University, Stanford, CA, USA.
Stéphane M MeystreDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Medical University of South Carolina, Charleston, SC, USA.

Funding

HSRD VA HIR 09-007HSRD VA IBE 09-069
6 · The paper itself

Abstract

Efforts to improve the treatment of congestive heart failure, a common and serious medical condition, include the use of quality measures to assess guideline-concordant care. The goal of this study is to identify left ventricular ejection fraction (LVEF) information from various types of clinical notes, and to then use this information for heart failure quality measurement. We analyzed the annotation differences between a new corpus of clinical notes from the Echocardiography, Radiology, and Text Integrated Utility package and other corpora annotated for natural language processing (NLP) research in the Department of Veterans Affairs. These reports contain varying degrees of structure. To examine whether existing LVEF extraction modules we developed in prior research improve the accuracy of LVEF information extraction from the new corpus, we created two sequence-tagging NLP modules trained with a new data set, with or without predictions from the existing LVEF extraction modules. We also conducted a set of experiments to examine the impact of training data size on information extraction accuracy. We found that less training data is needed when reports are highly structured, and that combining predictions from existing LVEF extraction modules improves information extraction when reports have less structured formats and a rich set of vocabulary.

Indexed as

Information Storage and RetrievalNatural Language ProcessingHeart FailureHumansStroke VolumeHeart failureMedical informaticsNatural language processingVentricular ejection fraction

Identifiers

PMID28163196
PMCPMC5575914

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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.