Evidence map›Paper›PMID 30843449›Full record

ArticleHealth informatics journal2020

Ontological representation-oriented term normalization and standardization of the Research Domain Criteria.

Fang Li, Guozheng Rao, Jingcheng Du, Yang Xiang, Yaoyun Zhang, Salih Selek, Jane Elizabeth Hamilton, Hua Xu, Cui Tao

Abstract read
In one paragraph

Article in Health informatics journal, 2020. 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

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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

9 authors.

Fang LiThe University of Texas Health Science Center at Houston, USA.ORCID 0000-0001-8865-7717
Guozheng RaoTianjin University, China.
Jingcheng Du
Yang Xiang
Yaoyun Zhang
Jane Elizabeth Hamilton
Hua Xu
Cui TaoThe University of Texas Health Science Center at Houston, USA.

Funding

Patient Medical History Representation, Extraction, and Inference from EHR DataR01LM011829 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI · 2014 to 2018
$1.7M
NLM NIH HHS R01 LM011829
6 · The paper itself

Abstract

The Research Domain Criteria, launched by the National Institute of Mental Health, is a new dimensional and interdisciplinary research framework for mental disorders. The Research Domain Criteria matrix is its core part. Since an ontology has the strengths of supporting semantic inferencing and automatic data processing, we would like to transform the Research Domain Criteria matrix into an ontological structure. In terms of data normalization, which is the essential part of an ontology representation, the Research Domain Criteria elements (mainly in the Units of Analysis) have some limitations. In this article, we propose a series of solutions to improve data normalization of the Research Domain Criteria elements in the Units of Analysis, including leveraging standard terminologies (i.e. the Unified Medical Language System Metathesaurus), context-combining queries, and domain expertise. The evaluation results show the positive (Yes) percentage is more than 80 percent, indicating our work is favorably received by the mental health professionals, and we have formed a good data foundation for the Research Domain Criteria ontological representation in the future work.

Indexed as

SemanticsUnified Medical Language SystemDatabases, FactualHumansResearchcontext querydata normalizationontological representationResearch Domain CriteriaUnified Medical Language System Metathesaurus

Identifiers

PMID30843449
PMCPMC7863676

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.