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Querying clinical data in HL7 RIM based relational model with morph-RDB.

ABSTRACT: Semantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One possible approach to accommodate this need is to use RDB2RDF systems that provide RDF datasets as the unified view. These RDF datasets may be materialized and stored in a triple store, or transformed into RDF in real time, as virtual RDF data sources. Our previous efforts involved materialized RDF datasets, hence losing data freshness.In this paper we present a solution that uses an ontology based on the HL7 v3 Reference Information Model and a set of R2RML mappings that relate this ontology to an underlying relational database implementation, and where morph-RDB is used to expose a virtual, non-materialized SPARQL endpoint over the data.By applying a set of optimization techniques on the SPARQL-to-SQL query translation algorithm, we can now issue SPARQL queries to the underlying relational data with generally acceptable performance.

SUBMITTER: Priyatna F

PROVIDER: S-EPMC5629785 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Querying clinical data in HL7 RIM based relational model with morph-RDB.

Priyatna Freddy F Alonso-Calvo Raul R Paraiso-Medina Sergio S Corcho Oscar O

Journal of biomedical semantics 20171005 1

<h4>Background</h4>Semantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One possible approach to accommodate this need is to use RDB2RDF systems that provide RDF datasets as the unified view. These RDF datasets may be materialized and stored in a triple store, or transformed into RDF in real time, as virtual RDF data s ...[more]

PMID: 28982381

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Project description:BackgroundThe recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained.MethodsOntologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner.ResultsWe developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease.ConclusionsThe proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.

Project description:BackgroundIn principle, gene expression data can be viewed as providing just the three-valued expression profiles of target biological elements relative to an experiment at hand. Although complicated, gathering expression profiles does not pose much of a challenge from a query language standpoint. What is interesting is how these expression profiles are used to tease out information from the vast array of information repositories that ascribe meaning to the expression profiles. Since such annotations are inherently experiment specific functions, much the same way as queries in databases, developing a querying system for gene expression data appears to be pointless. Instead, developing tools and techniques to support individual assignment has been considered prudent in contemporary research.ResultsWe propose a gene expression data management and querying system that is able to support pre-expression, expression and post-expression level analysis and reduce impedance mismatch between analysis systems. To this end, we propose a new, platform-independent and general purpose query language called Curray, for Custom Microarray query language, to support online expression data analysis using distributed resources. It includes features to design expression analysis pipelines using language constructs at the conceptual level. The ability to include user defined functions as a first-class language feature facilitates unlimited analysis support and removes language limitations. We show that Curray's declarative and extensible features nimbly allow flexible modeling and room for customization.ConclusionsThe developments proposed in this article allow users to view their expression data from a conceptual standpoint - experiments, probes, expressions, mapping, etc. at multiple levels of representation and independent of the underlying chip technologies. It also allows transparent roll-up and drill-down along representation hierarchies from raw data to standards such as MIAME and MAGE-ML using linguistic constructs. Curray also allows seamless integration with distributed web resources through its LifeDB system of which it is a part.

Dataset Information

Querying clinical data in HL7 RIM based relational model with morph-RDB.

Publications

Querying clinical data in HL7 RIM based relational model with morph-RDB.

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