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.
To improve the interoperability of EHRs represented with different standards, mappings between different standards are developed and domain ontologies are created. @cite_17 developed source ontologies for openEHR and ISO 13606, as well as a domain ontology which bridges the two standards. Data transformation is carried out through syntactic mapping between the archetypes of openEHR, ISO 13606 and the archetype model of the domain ontology. @cite_38 build up a domain ontology of ECG and map schemas of three ECG standards directly to the domain ontology. They find it difficult to develop a domain ontology to which different standards can directly be mapped.
@cite_38 @cite_17
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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. Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. It is a key challenge to achieve semantic interoperability in building the Semantic Web. This paper proposes a new generic and adaptive ontology mapping approach, called the PRIOR+, based on propagation theory, information retrieval techniques and artificial intelligence. The approach consists of three major modules, i.e., the IR-based similarity generator, the adaptive similarity filter and weighted similarity aggregator, and the neural network based constraint satisfaction solver. The approach first measures both linguistic and structural similarity of ontologies in a vector space model, and then aggregates them using an adaptive method based on their harmonies, which is defined as an estimator of performance of similarity. Finally to improve mapping accuracy the interactive activation and competition neural network is activated, if necessary, to search for a solution that can satisfy ontology constraints. The experimental results show that harmony is a good estimator of f-measure; the harmony based adaptive aggregation outperforms other aggregation methods; neural network approach significantly boosts the performance in most cases. Our approach is competitive with top-ranked systems on benchmark tests at OAEI campaign 2007, and performs the best on real cases in OAEI benchmark tests. This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs. Probabilities needed for constructing conditional probability tables (CPT) during translation and for measuring semantic similarity during mapping are learned using text classification techniques where each concept in an ontology is associated with a set of semantically relevant text documents, which are obtained by ontology guided web mining. The basic ideas of this approach are validated by positive results from computer experiments on two small real-world ontologies.