Display Omitted Provides multiple semantic representations of a data source with data virtualization.Supports data extraction and conversion in an automated way on-demand.Each step in the data flow is semantically processed, following the semantic web stack.The approach is tested on large clinical data with sound performance. 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.
In this paper we test the hypothesis that a domain reference ontology of the electrocardiogram (ECG) can be employed in an effective manner to achieve semantic integration between ECG data standards. Several standardization initiatives, namely AHA MIT-BIH (Physionet), SCP-ECG and HL7 aECG, have led to heterogeneous conceptualizations of the ECG domain. We then argue that a shared anchor, the biomedical reality under scrutiny, can effectively support the semantic integration of these ECG standards into a coherent ECG representation for the sake of a unified Electronic Health Record (EHR) model. Our hypothesis is tested by means of an integration experiment that uses, on the one hand, an ECG Ontology and, on the other hand, elicited conceptual models of the ECG standards. As a conclusion, we attest the hypothesis and also provide an integration table depicting correspondence links between entities in the ECG Ontology and elements in the ECG standards. The semantic interoperability between health information systems is a major challenge to improve the quality of clinical practice and patient safety. In recent years many projects have faced this problem and provided solutions based on specific standards and technologies in order to satisfy the needs of a particular scenario. Most of such solutions cannot be easily adapted to new scenarios, thus more global solutions are needed. In this work, we have focused on the semantic interoperability of electronic healthcare records standards based on the dual model architecture and we have developed a solution that has been applied to ISO 13606 and openEHR. The technological infrastructure combines reference models, archetypes and ontologies, with the support of Model-driven Engineering techniques. For this purpose, the interoperability infrastructure developed in previous work by our group has been reused and extended to cover the requirements of data transformation.