![]() Achieving interoperability for distributed EHR systems is expected to improve patient safety and care continuity, and therefore it improves the healthcare industry. Domain specialists validated the accuracy and correctness of the obtained results.Įlectronic Health Records (EHRs) aggregate the entire patient’s data from different systems. ![]() This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The used dataset includes identified data of 100 patients. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. However, this crisp ontology is not able to answer vague or uncertain queries. Second, a unified ontology is created that merges the previously created ontologies. ![]() First, a separate standard ontology is created for each input source. ![]() The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects.
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