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DMTO: a realistic ontology for standard diabetes mellitus treatment

Overview of attention for article published in Journal of Biomedical Semantics, February 2018
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Title
DMTO: a realistic ontology for standard diabetes mellitus treatment
Published in
Journal of Biomedical Semantics, February 2018
DOI 10.1186/s13326-018-0176-y
Pubmed ID
Authors

Shaker El-Sappagh, Daehan Kwak, Farman Ali, Kyung-Sup Kwak

Abstract

Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge. This paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients' current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology's BioPortal at http://bioportal.bioontology.org/ontologies/DMTO . The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs. The ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 152 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 152 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 13%
Student > Master 19 13%
Student > Ph. D. Student 16 11%
Student > Bachelor 12 8%
Student > Doctoral Student 6 4%
Other 26 17%
Unknown 53 35%
Readers by discipline Count As %
Computer Science 39 26%
Engineering 14 9%
Nursing and Health Professions 11 7%
Medicine and Dentistry 9 6%
Agricultural and Biological Sciences 4 3%
Other 19 13%
Unknown 56 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 May 2018.
All research outputs
#13,579,722
of 23,020,670 outputs
Outputs from Journal of Biomedical Semantics
#196
of 364 outputs
Outputs of similar age
#218,779
of 437,329 outputs
Outputs of similar age from Journal of Biomedical Semantics
#6
of 10 outputs
Altmetric has tracked 23,020,670 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 437,329 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.