An Extended Semantic Interoperability Model for Distributed Electronic Health Record Based on Fuzzy Ontology Semantics
| Autor: | Shaker El-Sappagh, Mohammed Elmogy, Jong Wan Hu, Sherif Barakat, Ebtsam Adel |
|---|---|
| Přispěvatelé: | Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información |
| Rok vydání: | 2021 |
| Předmět: |
Correctness
TK7800-8360 Computer Networks and Communications Computer science 02 engineering and technology Ontology (information science) Semantics Fuzzy logic SPARQL 020204 information systems 0202 electrical engineering electronic engineering information engineering Fuzzy ontology Electrical and Electronic Engineering Electronic health record (EHR) Class (computer programming) electronic health record (EHR) Information retrieval fuzzy ontology computer.file_format Semantic interoperability openEHR semantic Hardware and Architecture Control and Systems Engineering Signal Processing 020201 artificial intelligence & image processing Electronics computer Semantic |
| Zdroj: | Minerva: Repositorio Institucional de la Universidad de Santiago de Compostela Universidad de Santiago de Compostela (USC) Electronics; Volume 10; Issue 14; Pages: 1733 Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela instname Electronics, Vol 10, Iss 1733, p 1733 (2021) |
| Popis: | Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. 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 resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained results This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2021R1A2B5B02002599) SI |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|