Evidence map›Paper›PMID 25750622›Full record

ArticleFrontiers in neuroinformatics2015

Medical data transformation using rewriting.

Naveen Ashish, Arthur W Toga

Abstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Naveen AshishLaboratory of Neuroimaging, Institute for Neuroimaging and Neuroinformatics, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, USA.
Arthur W TogaLaboratory of Neuroimaging, Institute for Neuroimaging and Neuroinformatics, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, USA.

Funding

TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2012 to 2022
$14.1M
TrainingU54EB020406 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TOGA, ARTHUR W · 2014 to 2018
$12.5M
NIBIB NIH HHS P41 EB015922NIBIB NIH HHS U54 EB020406
6 · The paper itself

Abstract

This paper presents a system for declaratively transforming medical subjects' data into a common data model representation. Our work is part of the "GAAIN" project on Alzheimer's disease data federation across multiple data providers. We present a general purpose data transformation system that we have developed by leveraging the existing state-of-the-art in data integration and query rewriting. In this work we have further extended the current technology with new formalisms that facilitate expressing a broader range of data transformation tasks, plus new execution methodologies to ensure efficient data transformation for disease datasets.

Indexed as

Alzheimer's disease datasetsdata integrationdata mappingquery rewriting

Identifiers

PMID25750622
PMCPMC4335467

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.