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Developing a modular architecture for creation of rule-based clinical diagnostic criteria

Overview of attention for article published in BioData Mining, October 2016
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Title
Developing a modular architecture for creation of rule-based clinical diagnostic criteria
Published in
BioData Mining, October 2016
DOI 10.1186/s13040-016-0113-5
Pubmed ID
Authors

Na Hong, Jyotishman Pathak, Christopher G. Chute, Guoqian Jiang

Abstract

With recent advances in computerized patient records system, there is an urgent need for producing computable and standards-based clinical diagnostic criteria. Notably, constructing rule-based clinical diagnosis criteria has become one of the goals in the International Classification of Diseases (ICD)-11 revision. However, few studies have been done in building a unified architecture to support the need for diagnostic criteria computerization. In this study, we present a modular architecture for enabling the creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies. The architecture consists of two modules: an authoring module that utilizes a standards-based information model and a translation module that leverages Semantic Web Rule Language (SWRL). In a prototype implementation, we created a diagnostic criteria upper ontology (DCUO) that integrates ICD-11 content model with the Quality Data Model (QDM). Using the DCUO, we developed a transformation tool that converts QDM-based diagnostic criteria into Semantic Web Rule Language (SWRL) representation. We evaluated the domain coverage of the upper ontology model using randomly selected diagnostic criteria from broad domains (n = 20). We also tested the transformation algorithms using 6 QDM templates for ontology population and 15 QDM-based criteria data for rule generation. As the results, the first draft of DCUO contains 14 root classes, 21 subclasses, 6 object properties and 1 data property. Investigation Findings, and Signs and Symptoms are the two most commonly used element types. All 6 HQMF templates are successfully parsed and populated into their corresponding domain specific ontologies and 14 rules (93.3 %) passed the rule validation. Our efforts in developing and prototyping a modular architecture provide useful insight into how to build a scalable solution to support diagnostic criteria representation and computerization.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 22%
Researcher 5 16%
Student > Ph. D. Student 4 13%
Other 3 9%
Student > Postgraduate 3 9%
Other 6 19%
Unknown 4 13%
Readers by discipline Count As %
Computer Science 10 31%
Medicine and Dentistry 5 16%
Agricultural and Biological Sciences 3 9%
Biochemistry, Genetics and Molecular Biology 2 6%
Business, Management and Accounting 1 3%
Other 4 13%
Unknown 7 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 22 October 2016.
All research outputs
#18,382,900
of 22,769,322 outputs
Outputs from BioData Mining
#259
of 307 outputs
Outputs of similar age
#239,002
of 316,039 outputs
Outputs of similar age from BioData Mining
#8
of 8 outputs
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