Evidence map›Paper›PMID 25817919›Full record

ArticleJournal of biomedical informatics2015

An integrated, ontology-driven approach to constructing observational databases for research.

William Hsu, Nestor R Gonzalez, Aichi Chien, J Pablo Villablanca, Päivi Pajukanta, Fernando Viñuela, Alex A T Bui

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
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

7 citing papers in PubMed, 22 citations in OpenAlex.

  1. Predictors of new persistent opioid use after surgery in adults.Anesthesiology and perioperative science · 2025
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  4. Merging heterogeneous clinical data to enable knowledge discovery.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019
    Article
  5. Article
  6. Article
  7. 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

7 authors at 1 institution in 1 country.

William HsuDepartment of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, United States. Electronic address: willhsu@mii.ucla.edu.
Nestor R GonzalezDepartment of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, United States; Department of Neurosurgery, UCLA David Geffen School of Medicine, Los Angeles, CA, United States.
Aichi ChienDepartment of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, United States.
J Pablo VillablancaDepartment of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, United States.
Päivi PajukantaDepartment of Human Genetics, UCLA David Geffen School of Medicine, Los Angeles, CA, United States.
Fernando ViñuelaDepartment of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, United States.
Alex A T BuiDepartment of Radiological Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, United States.
University of California, Los Angeles · US

Funding

Systems genomics of metabolic syndrome traitsP01HL028481 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI REUE, KAREN · 1985 to 2019
$50.2M
Genomics of dyslipidemia in MexicansR01HL095056 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PAJUKANTA, PAIVI · 2009 to 2018
$5.9M
Toward Individually-tailored Medicine: Probabilistic Models of Cerebral AneurysmsR01EB000362 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUI, ALEX · 2003 to 2012
$5.1M
A Predictive Prognostic Model for Glioblastoma MultiformeR01CA157553 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUI, ALEX · 2012 to 2016
$2.2M
NCI NIH HHS R01 CA157553NHLBI NIH HHS P01 HL-028481NHLBI NIH HHS P01 HL028481NHLBI NIH HHS R01 HL-095056NHLBI NIH HHS R01 HL095056NIBIB NIH HHS R01 EB000362
6 · The paper itself

Abstract

The electronic health record (EHR) contains a diverse set of clinical observations that are captured as part of routine care, but the incomplete, inconsistent, and sometimes incorrect nature of clinical data poses significant impediments for its secondary use in retrospective studies or comparative effectiveness research. In this work, we describe an ontology-driven approach for extracting and analyzing data from the patient record in a longitudinal and continuous manner. We demonstrate how the ontology helps enforce consistent data representation, integrates phenotypes generated through analyses of available clinical data sources, and facilitates subsequent studies to identify clinical predictors for an outcome of interest. Development and evaluation of our approach are described in the context of studying factors that influence intracranial aneurysm (ICA) growth and rupture. We report our experiences in capturing information on 78 individuals with a total of 120 aneurysms. Two example applications related to assessing the relationship between aneurysm size, growth, gene expression modules, and rupture are described. Our work highlights the challenges with respect to data quality, workflow, and analysis of data and its implications toward a learning health system paradigm.

Indexed as

Databases, FactualVocabulary, ControlledAneurysm, RupturedBiomedical ResearchData AccuracyDatabase Management SystemsData MiningElectronic Health RecordsHumansIntracranial AneurysmMeaningful UseNatural Language ProcessingSystems IntegrationUser-Computer InterfaceBiomedical ontologyDatabaseData extractionImage analysisIntracranial aneurysmRetrospective study

Identifiers

PMID25817919
PMCPMC4464942
OpenAlexW2070398892

What OpenQuestion holds

Textmetadata
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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.