Evidence map›Paper›PMID 21498805›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA

Application of statistical machine translation to public health information: a feasibility study.

Katrin Kirchhoff, Anne M Turner, Amittai Axelrod, Francisco Saavedra

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. When is good, good enough? On considerations of machine translation in patient education.Journal of cancer education : the official journal of the American Association for Cancer Education · 2024
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  11. Review
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  14. PHAST: A Collaborative Machine Translation and Post-Editing Tool for Public Health.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015
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  20. Clinical research informatics: a conceptual perspective.Journal of the American Medical Informatics Association : JAMIA · 2012
    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.

Katrin KirchhoffDepartment of Electrical Engineering, University of Washington, Seattle, Washington 98195, USA. katrin@ee.washington.edu
Anne M Turner
Amittai Axelrod
Francisco Saavedra

Funding

University of Washington Biomedical Informatics and Data Science Research Training ProgramT15LM007442 · NLM · UNIVERSITY OF WASHINGTON · PI JOHN H. GENNARI, Andrea L. Hartzler · 2002 to 2026
$19.6M
Improving access to multi-lingual health information through machine translationR01LM010811 · NLM · UNIVERSITY OF WASHINGTON · PI KIRCHHOFF, KATRIN, TURNER, ANNE M · 2010 to 2014
$1.7M
NLM NIH HHS 1R01LM010811-01NLM NIH HHS R01 LM010811NLM NIH HHS T15 LM007442NLM NIH HHS T15 LM007442-07OPHPR CDC HHS P01 TP000297OPHPR CDC HHS P01TP000297PHITPO CDC HHS P01 HK 000027PHITPO CDC HHS P01 HK000027
6 · The paper itself

Abstract

objectiveAccurate, understandable public health information is important for ensuring the health of the nation. The large portion of the US population with Limited English Proficiency is best served by translations of public-health information into other languages. However, a large number of health departments and primary care clinics face significant barriers to fulfilling federal mandates to provide multilingual materials to Limited English Proficiency individuals. This article presents a pilot study on the feasibility of using freely available statistical machine translation technology to translate health promotion materials.

designThe authors gathered health-promotion materials in English from local and national public-health websites. Spanish versions were created by translating the documents using a freely available machine-translation website. Translations were rated for adequacy and fluency, analyzed for errors, manually corrected by a human posteditor, and compared with exclusively manual translations.

resultsMachine translation plus postediting took 15-53 min per document, compared to the reported days or even weeks for the standard translation process. A blind comparison of machine-assisted and human translations of six documents revealed overall equivalency between machine-translated and manually translated materials. The analysis of translation errors indicated that the most important errors were word-sense errors.

conclusionThe results indicate that machine translation plus postediting may be an effective method of producing multilingual health materials with equivalent quality but lower cost compared to manual translations.

Indexed as

AutomationConsumer Health InformationHealth PromotionMultilingualismTranslatingFeasibility StudiesHumansInternetPilot ProjectsUnited States

Identifiers

PMID21498805
PMCPMC3128406

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.