An encoding methodology for medical knowledge using SNOMED CT ontology

El–Sappagh, Shaker; Elmogy, Mohammed

doi:10.1016/j.jksuci.2015.10.002

Cited by 7 publications

(5 citation statements)

References 35 publications

(57 reference statements)

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“…The paper selected SNOMED CT (SCT) as a unified language for many reasons. SCT has the most comprehensive terminology in the world and provides the most coverage of medical terms [ 54 ]. Lee et al [ 55 ] surveyed SCT implementations in terms of design, use, and maintenance issues.…”

Section: Introductionmentioning

confidence: 99%

DMTO: a realistic ontology for standard diabetes mellitus treatment

et al. 2018

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BackgroundTreatment 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.ResultsThis 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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (10.1186/s13326-018-0176-y) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

DMTO: a realistic ontology for standard diabetes mellitus treatment

et al. 2018

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“…Since this research study introduces an ontology-based methodology, the ontology evaluation is essential to verify the validity and the completeness of the ontology. Encoding for Medical Information SNOMED CT converts case-based text features into SCT IDs and was proposed to generate a semantic reuse mechanism [ 31 ]. These authors used SNOMED CT coding to encode event-based knowledge and applied it to the diabetes diagnostic event-based dataset.…”

Section: Discussion Conclusion and Future Directionsmentioning

confidence: 99%

An Ontological Clinical Decision Support System Based on Clinical Guidelines for Diabetes Patients in Sri Lanka

et al. 2020

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Health professionals should follow the clinical guidelines to decrease healthcare costs to avoid unnecessary testing and to minimize the variations among healthcare providers. In addition, this will minimize the mistakes in diagnosis and treatment processes. To this end, it is possible to use Clinical Decision Support Systems that implement the clinical guidelines. Clinical guidelines published by international associations are not suitable for developing countries such as Sri Lanka, due to the economic background, lack of resources, and unavailability of some laboratory tests. Hence, a set of clinical guidelines has been formulated based on the various published international professional organizations from a Sri Lankan context. Furthermore, these guidelines are usually presented in non-computer-interpretable narrative text or non-executable flow chart formats. In order to fill this gap, this research study finds a suitable approach to represent/organize the clinical guidelines in a Sri Lankan context that is suitable to be used in a clinical decision support system. To this end, we introduced a novel approach which is an ontological model based on the clinical guidelines. As it is revealed that there are 4 million diabetes patients in Sri Lanka, which is approximately twenty percent of the total population, we used diabetes-related guidelines in this research. Firstly, conceptual models were designed to map the acquired diabetes-related clinical guidelines using Business Process Model and Notation 2.0. Two models were designed in mapping the diagnosis process of Type 1 and Type 2 Diabetes, and Gestational diabetes. Furthermore, several conceptual models were designed to map the treatment plans in guidelines by using flowcharting. These designs were validated by domain experts by using questionnaires. Grüninger and Fox’s method was used to design and evaluate the ontology based on the designed conceptual models. Domain experts’ feedback and several real-life diabetic scenarios were used to validate and evaluate the developed ontology. The evaluation results show that all suggested answers based on the proposed ontological model are accurate and well addressed with respect to the real-world scenarios. A clinical decision support system was implemented based on the ontological knowledge base using the Jena Framework, and this system can be used to access the diabetic information and knowledge in the Sri Lankan context. However, this contribution is not limited to diabetes or a local context, and can be applied to any disease or any context.

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“…Top citer for the first cluster was (19) from Helwan University, Cairo, Egypt: presenting a review on swarm and evolutionary computing approaches for deep learning (19). In cluster 2, the most active citer was El-Sappagh et al based at Minia University in Egypt, who published three review papers on medical case reasoning frameworks, SNOMED CT ontology and mobile health technologies for diabetes mellitus (20)(21)(22). The last subcluster was actively cited by Syaed et al based at Cairo University in Egypt, who published his empirical work on binary whale optimization algorithm and binary moth flame optimization with clustering algorithms for clinical breast cancer diagnoses (23) (Figures 5 and 6).…”

Section: Clusters Of Research In Telehealthmentioning

confidence: 99%

Telemedicine in Arab Countries: Innovation, Research Trends, and Way Forward

Ahmed¹,

Mehmood²,

Jawwad³

et al. 2021

Front. Digit. Health

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Background: The progress and innovation in telemedicine within the Middle Eastern countries have not been heavily monitored. Therefore, the present study aims to analyze the scholarly work conducted in the Arab world, using reproducible statistical and scientometric techniques.Methods: An electronic search of Web of Science (core database) had been conducted through use of an extensive search strategy comprising of keywords specific to the Arab region, EMRO countries, telehealth, medical conditions, and disorders. A total yield of 1,630 search results were processed, indexed through July 7, 2020. CiteSpace (5.7.R1, Drexel University, Pennsylvania, USA) is a Java-based application, a user-friendly tool for conducting scientometric analyses.Results: The present analyses found a lack of innovation in the field of digital health in the Arab countries. Many gaps in research were found in Arab countries, which will be discussed subsequently. Digital health research was clustered around themes of big data and artificial intelligence; a lack of progress was seen in telemedicine and digital health. Furthermore, only a small proportion of these publications had principal or corresponding authors from Arab countries. A clear disparity in digital health research in the Arab world was evident after comparing these insights with our previous investigation on telemedicine research in the global context.Conclusion: Telemedicine research is still in its infancy in the Middle Eastern countries. Recommendations include diversification of the research landscape and interdisciplinary collaborations in this area.

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An encoding methodology for medical knowledge using SNOMED CT ontology

Cited by 7 publications

References 35 publications

DMTO: a realistic ontology for standard diabetes mellitus treatment

DMTO: a realistic ontology for standard diabetes mellitus treatment

An Ontological Clinical Decision Support System Based on Clinical Guidelines for Diabetes Patients in Sri Lanka

Telemedicine in Arab Countries: Innovation, Research Trends, and Way Forward

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