Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching

Kiourtis, Athanasios; Nifakos, Sokratis; Mavrogiorgou, Argyro; Kyriazis, Dimosthenis

doi:10.1016/j.ijmedinf.2019.104002

Cited by 43 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The API supported “button-type population health” as the core data elements can be quickly and standardly extracted from electronic health records, enabling local, regional, and national data-driven innovation. Kiourtis et al [ 29 ] proposed a mechanism to aggregate the semantic and syntactic similarity in healthcare data and transform it into corresponding HL7 FHIR architecture to promise healthcare interoperability. They further verified the quality of the proposed mechanism with aligned APIs and a non-dominated sorting genetic algorithm (NSGA-III) to achieve ontology alignment.…”

Section: Related Workmentioning

confidence: 99%

HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study

Chatterjee

Pahari

Prinz

2022

Sensors

View full text Add to dashboard Cite

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) smartphone application to prove the concept. We combine HL7 FHIR and SNOMED-CT vocabularies to exchange personal health data in JavaScript object notion (JSON). This study explores and analyzes our attempt to design and implement a structurally and logically compatible tethered personal health record (PHR) that allows bidirectional communication with an EHR. Our eCoach prototype implements most PHR-S FM functions as an interoperability quality standard. Its end-to-end (E2E) data are protected with a TSD (Services for Sensitive Data) security mechanism. We achieve 0% data loss and 0% unreliable performances during data transfer between PHR and EHR. Furthermore, this experimental study shows the effectiveness of FHIR modular resources toward flexible management of data components in the PHR (eCoach) prototype.

show abstract

Section: Related Workmentioning

confidence: 99%

HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study

Chatterjee

Pahari

Prinz

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In some prior literature, many RDF datasets were created using Apache JENA 4.0 [ 4 ], different versions of protégé were used to construct and represent various healthcare ontologies [ 2 , 17 ], Apache Jena framework was used for OWL reasoning on the RDF datasets [ 50 , 53 ], and the EYE engine was used for reasoning [ 54 ]. Besides, Kiourtis et al [ 68 ] developed a technique for converting healthcare data into its equivalent HL7 FHIR structure, which principally corresponds to the most used data structures for describing healthcare information. Furthermore, a sublanguage of F-logic named Frame Logic for Semantic Web Services (FLOG4SWS) and web services along with some features of Flora-2 was used to represent the ontology [ 51 ].…”

Section: Analysis Of the Selected Articles: Thematic Areasmentioning

confidence: 99%

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues

Haque

Arifuzzaman

Siddik

et al. 2022

International Journal of Clinical Practice

View full text Add to dashboard Cite

Today, healthcare has become one of the largest and most fast-paced industries due to the rapid development of digital healthcare technologies. The fundamental thing to enhance healthcare services is communicating and linking massive volumes of available healthcare data. However, the key challenge in reaching this ambitious goal is letting the information exchange across heterogeneous sources and methods as well as establishing efficient tools and techniques. Semantic Web (SW) technology can help to tackle these problems. They can enhance knowledge exchange, information management, data interoperability, and decision support in healthcare systems. They can also be utilized to create various e-healthcare systems that aid medical practitioners in making decisions and provide patients with crucial medical information and automated hospital services. This systematic literature review (SLR) on SW in healthcare systems aims to assess and critique previous findings while adhering to appropriate research procedures. We looked at 65 papers and came up with five themes: e-service, disease, information management, frontier technology, and regulatory conditions. In each thematic research area, we presented the contributions of previous literature. We emphasized the topic by responding to five specific research questions. We have finished the SLR study by identifying research gaps and establishing future research goals that will help to minimize the difficulty of adopting SW in healthcare systems and provide new approaches for SW-based medical systems’ progress.

show abstract

“…During the full data cleaning workflow, the data being ingested may be streaming (e.g., Twitter and Facebook) or coming from an originally stored dataset (e.g., webpages, blogs, and local datastores). Through the ingestion process, the dataset’s domain is identified following the approach of Kiourtis et al (2019) by discovering and analyzing the semantics of the ingested data. As a result, following the process indicated in Mavrogiorgou et al (2021), where the domain of the dataset is identified, the required set of data cleaning actions is computed.…”

Section: Policycloud Ingest Analyticsmentioning

confidence: 99%

PolicyCLOUD: A prototype of a cloud serverless ecosystem for policy analytics

et al. 2022

Self Cite

View full text Add to dashboard Cite

We present PolicyCLOUD: a prototype for an extensible serverless cloud-based system that supports evidence-based elaboration and analysis of policies. PolicyCLOUD allows flexible exploitation and management of policy-relevant dataflows, by enabling the practitioner to register datasets and specify a sequence of transformations and/or information extraction through registered ingest functions. Once a possibly transformed dataset has been ingested, additional insights can be retrieved by further applying registered analytic functions to it. PolicyCLOUD was built as an extensible framework toward the creation of an analytic ecosystem. As of now, we have developed several essential ingest and analytic functions that are built-in within the framework. They include data cleaning, enhanced interoperability, and sentiment analysis generic functions; in addition, a trend analysis function is being created as a new built-in function. PolicyCLOUD has also the ability to tap on the analytic capabilities of external tools; we demonstrate this with a social dynamics tool implemented in conjunction with PolicyCLOUD, and describe how this stand-alone tool can be integrated with the PolicyCLOUD platform to enrich it with policy modeling, design and simulation capabilities. Furthermore, PolicyCLOUD is supported by a tailor-made legal and ethical framework derived from privacy/data protection best practices and existing standards at the EU level, which regulates the usage and dissemination of datasets and analytic functions throughout its policy-relevant dataflows. The article describes and evaluates the application of PolicyCLOUD to four families of pilots that cover a wide range of policy scenarios.

show abstract

Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching

Cited by 43 publications

References 18 publications

HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study

HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues

PolicyCLOUD: A prototype of a cloud serverless ecosystem for policy analytics

Contact Info

Product

Resources

About