Search results for "SNOMED"
showing 3 items of 3 documents
HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study
2022
Heterogeneity is a problem in storing and exchanging data in a digital health information system (HIS) following semantic and structural integrity. The existing literature shows different methods to overcome this problem. Fast healthcare interoperable resources (FHIR) as a structural standard may explain other information models, (e.g., personal, physiological, and behavioral data from heterogeneous sources, such as activity sensors, questionnaires, and interviews) with semantic vocabularies, (e.g., Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT)) to connect personal health data to an electronic health record (EHR). We design and develop an intuitive health coaching (eCoach…
SNOMED on FHIR Transmission of clinical data with the Fast Healthcare Interoperability Resources protocol (HL7-FHIR) utilizing Systematized Nomenclat…
2019
Master's thesis Information- and communication technology IKT590 - University of Agder 2019 The exchange of information at the level of semantic interoperability needs some infor-mation model and clinical terminology work together. Electronic records of the patientmust include the terminologies in order to give the decision support to the practitionerwhen needed. Due to the lack of common standards for data structure and data sharingcreates the problem to interact and share information with different applications.Our main goal in this thesis is transferring the SNOMED-CT (Systematized Nomenclatureof Medicine Clinical Terms) terminologies using HL7 FHIR (Fast Healthcare Interoper-ability Res…
An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-C…
2020
Background Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover…