Evidence map›Paper›PMID 28764813›Full record

ArticleJournal of biomedical semantics2017

An ontology-driven tool for structured data acquisition using Web forms.

Rafael S Gonçalves, Samson W Tu, Csongor I Nyulas, Michael J Tierney, Mark A Musen

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. The Data Gap in the EHR for Clinical Research Eligibility Screening.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018
    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.

Rafael S GonçalvesStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA. rafael.goncalves@stanford.edu.ORCID http://orcid.org/0000-0003-1255-0125
Samson W TuStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Csongor I NyulasStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Michael J TierneyStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Mark A MusenStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

Funding

Collaborative Development of Biomedical Ontologies and TerminologiesR01GM086587 · NIGMS · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2009 to 2016
$4.1M
Protege: A Knowledge-Engineering Environment for Advancing Biomedical SciencesR01GM121724 · NIGMS · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2017 to 2020
$2.3M
Protege: An Ontology-Development Platform for Biomedical ScientistsR01GM103316 · NIGMS · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2013 to 2016
$2.1M
NIGMS NIH HHS R01 GM086587NIGMS NIH HHS R01 GM103316NIGMS NIH HHS R01 GM121724
6 · The paper itself

Abstract

backgroundStructured data acquisition is a common task that is widely performed in biomedicine. However, current solutions for this task are far from providing a means to structure data in such a way that it can be automatically employed in decision making (e.g., in our example application domain of clinical functional assessment, for determining eligibility for disability benefits) based on conclusions derived from acquired data (e.g., assessment of impaired motor function). To use data in these settings, we need it structured in a way that can be exploited by automated reasoning systems, for instance, in the Web Ontology Language (OWL); the de facto ontology language for the Web.

resultsWe tackle the problem of generating Web-based assessment forms from OWL ontologies, and aggregating input gathered through these forms as an ontology of "semantically-enriched" form data that can be queried using an RDF query language, such as SPARQL. We developed an ontology-based structured data acquisition system, which we present through its specific application to the clinical functional assessment domain. We found that data gathered through our system is highly amenable to automatic analysis using queries.

conclusionsWe demonstrated how ontologies can be used to help structuring Web-based forms and to semantically enrich the data elements of the acquired structured data. The ontologies associated with the enriched data elements enable automated inferences and provide a rich vocabulary for performing queries.

Indexed as

Biological OntologiesInternetSoftwareInformation Storage and RetrievalData acquisitionForm generationOntologyOWLStructured data

Identifiers

PMID28764813
PMCPMC5540339

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.