SNOMED CT standard ontology based on the ontology for general medical science

El–Sappagh, Shaker; Franda, Francesco; Ali, Farman; Kwak, Kyung Sup

doi:10.1186/s12911-018-0651-5

Cited by 73 publications

(48 citation statements)

References 40 publications

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“…Our previous studies demonstrated the benefits of using an ontology to build CDSSs [9, 10, 65, 66]. The formal and explicit semantics facilitate knowledge representation, sharing, and reuse.…”

Section: Discussionmentioning

confidence: 99%

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

El–Sappagh¹,

Ali²,

Hendawi³

et al. 2019

BMC Med Inform Decis Mak

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Background Mobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs. Methods This paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology. Results This realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients’ wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at https://bioportal.bioontology.org/ontologies/FASTO . Conclusions The resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies. Electronic supplementary material The online version of this article (10.1186/s12911-019-0806-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

El–Sappagh¹,

Ali²,

Hendawi³

et al. 2019

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

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“…According to Linked Data principles and best practices [ 42 ], the proposed Smart Health (SH) KG (available at ) reuses and combines well-known KGs. Basically, the Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT) [ 56 ] has been adopted as upper ontology to model the taxonomy of both human anatomy and medical specialties. Moreover, specific clinical terms have been borrowed from the Uber Anatomy Ontology (UBERON) [ 57 ] and the Foundational Model of Anatomy (FMA) [ 58 ], extending the basic anatomical taxonomy with additional entities about human systems.…”

Section: Proposed Approachmentioning

confidence: 99%

Knowledge-Based Decision Support in Healthcare via Near Field Communication

Loseto

Scioscia

Ruta

et al. 2020

Sensors

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The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient’s case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care.

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“…This objective would provide great advantages for the outcome of each component in an EHR ecosystem. Hence, there is a great need for standards, ontologies, and medical terminologies [201]. 4.…”

Section: Mh Adds Another Level Of Complexity By Addingmentioning

confidence: 99%

“…Semantically intelligent CDSSs can use ontologies to infer hidden information with semantic relations by using description logic axioms, such as drug-drug interactions and comorbidity [36], [201], [202]. 4.…”

Section: B Mh and Cdss Challenges And Future Directionsmentioning

confidence: 99%

Mobile Health Technologies for Diabetes Mellitus: Current State and Future Challenges

et al. 2019

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The prevalence of diabetes is rising globally. Diabetes patients need continuous monitoring, and to achieve this objective, they have to be engaged in their healthcare management process. Mobile health (MH) is an information and communications technology trend to empower chronically ill patients in a smart environment. Discussing the current state of MH technologies is required in order to address their limitations. Existing review articles have evaluated the MH literature based on applicability and level of adoption by patients and healthcare providers. Most of these reviews asserted that MH apps and research have not reached a stable level yet. To the best of our knowledge, there is no clear description of solutions to these problems. In addition, no one has investigated and analyzed MH in its contextual environment in a detailed way. We conducted a comprehensive survey of MH research on diabetes management articles published between 2011 and September 27, 2017. In this survey, we discuss current challenges in MH, along with research gaps, opportunities, and trends. Our literature review searched three academic databases (ScienceDirect, IEEE Xplore, and SpringerLink). A total of 60 articles were analyzed, with 30% from ScienceDirect, 38% from IEEE Xplore, and 32% from SpringerLink. MH was analyzed in the context of the electronic health record (EHR) ecosystem. We consider dimensions such as clinical decision support systems, EHRs, cloud computing, semantic interoperability, wireless body area networks, and big data analytics. We propose specific metrics to analyze and evaluate MH from each of these dimensions. A comprehensive analysis of the literature from this viewpoint is valuable for both theoretical and developmental progress. This paper provides a critical analysis of challenges that have not been fully met and highlights directions for future research that could improve MH applicability. INDEX TERMS Mobile health, diabetes mellitus, electronic health record, cloud computing, clinical decision support system, big data analytics, wireless body area network, medical informatics.

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SNOMED CT standard ontology based on the ontology for general medical science

Cited by 73 publications

References 40 publications

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

Knowledge-Based Decision Support in Healthcare via Near Field Communication

Mobile Health Technologies for Diabetes Mellitus: Current State and Future Challenges

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