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Hussain S.,French Institute of Health and Medical Research | Sun H.,Advanced Clinical Applications Research Group | Sinaci A.,Development and Consultancy Ltd. | Erturkmen G.B.L.,Development and Consultancy Ltd. | And 6 more authors.
Studies in Health Technology and Informatics | Year: 2014

Use of medical terminologies and mappings across them are considered to be crucial pre-requisites for achieving interoperable eHealth applications. Built upon the outcomes of several research projects, we introduce a framework for evaluating and utilizing terminology mappings that offers a platform for i) performing various mappings strategies, ii) representing terminology mappings together with their provenance information, and iii) enabling terminology reasoning for inferring both new and erroneous mappings. We present the results of the introduced framework from SALUS project where we evaluated the quality of both existing and inferred terminology mappings among standard terminologies. © 2014 European Federation for Medical Informatics and IOS Press. Source


Yuksel M.,SRDC Software Research and Development and Consultancy Ltd. | Gonul S.,SRDC Software Research and Development and Consultancy Ltd. | Gonul S.,Middle East Technical University | Laleci Erturkmen G.B.,SRDC Software Research and Development and Consultancy Ltd. | And 7 more authors.
BioMed Research International | Year: 2016

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information. © 2016 Mustafa Yuksel et al. Source


Yuksel M.,SRDC Software Research and Development and Consultancy Ltd. | Yuksel M.,Middle East Technical University | Gonul S.,SRDC Software Research and Development and Consultancy Ltd. | Gonul S.,Middle East Technical University | And 6 more authors.
CEUR Workshop Proceedings | Year: 2013

This work aims to demonstrate the interoperability framework devel-oped in the SALUS project which enables effective integration and utilization of EHR data to reinforce post-market safety activities. Source


De Potter P.,Ghent University | Cools H.,Advanced Clinical Applications Research Group | Depraetere K.,Advanced Clinical Applications Research Group | Mels G.,Advanced Clinical Applications Research Group | And 6 more authors.
Computer Methods and Programs in Biomedicine | Year: 2012

Although the health care sector has already been subjected to a major computerization effort, this effort is often limited to the implementation of standalone systems which do not communicate with each other. Interoperability problems limit health care applications from achieving their full potential. In this paper, we propose the use of Semantic Web technologies to solve interoperability problems between data providers. Through the development of unifying health care ontologies, data from multiple health care providers can be aggregated, which can then be used as input for a decision support system. This way, more data is taken into account than a single health care provider possesses in his local setting. The feasibility of our approach is demonstrated by the creation of an end-to-end proof of concept, focusing on Belgian health care providers and medicinal decision support. © 2012 Elsevier Ireland Ltd. Source


Sun H.,Advanced Clinical Applications Research Group | Depraetere K.,Advanced Clinical Applications Research Group | De Roo J.,Advanced Clinical Applications Research Group | Mels G.,Advanced Clinical Applications Research Group | And 3 more authors.
Journal of Biomedical Informatics | Year: 2015

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data. © 2015 Elsevier Inc. Source

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