Semantic enrichment of longitudinal clinical study data using the CDISC standards and the semantic statistics vocabularies

Abstract

BackgroundThere is an increasing recognition of the need for the data capture phase of clinical studies to be improved and for more effective sharing of clinical data. The Health Care and Life Sciences community has embraced semantic technologies to facilitate the integration of health data from electronic health records, clinical studies and pharmaceutical research. This paper explores the integration of clinical study data exchange standards and semantic statistic vocabularies to deliver clinical data as linked data in a format that is easier to enrich with links to complementary data sources and consume by a broad user base.MethodsWe propose a Linked Clinical Data Cube (LCDC), which combines the strength of the RDF Data Cube and DDI-RDF vocabulary to enrich clinical data based on the CDISC standards. The CDISC standards provide the mechanisms for the data to be standardised, made more accessible and accountable whereas the RDF Data Cube and DDI-RDF vocabularies provide novel approaches to managing large volumes of heterogeneous linked data resources.ResultsWe validate our approach using a large-scale longitudinal clinical study into neurodegenerative diseases. This dataset, comprising more than 1600 variables clustered in 25 different sub-domains, has been fully converted into RDF forming one main data cube and one specialised cube for each sub-domain. One sub-domain, the Medications specialised cube, has been linked to relevant external vocabularies, such as the Australian Medicines Terminology and the ATC DDD taxonomy and DrugBank terminology. This provides new dimensions on which to query the data that promote the exploration of drug-drug and drug-disease interactions.ConclusionsThis implementation highlights the effectiveness of the association of the semantic statistics vocabularies for the publication of large heterogeneous data sets as linked data and the integration of the semantic statistics vocabularies with the CDISC standards. In particular, it demonstrates the potential of the two vocabularies in overcoming the monolithic nature of the underlying model and improving the navigation and querying of the data from multiple angles to support richer data analysis of clinical study data. The forecasted benefits are more efficient use of clinicians’ time and the potential to facilitate cross-study analysis.

Extending XNAT Platform with an Incremental Semantic Framework

“…Concept generalization (class subsumption in ontologies) and the graph-based model of RDF provide a powerful and flexible environment for query design. The use of ontologies and SPARQL for “intelligent querying” has been demonstrated many times in the literature (Pathak et al, 2012a , b ; Leroux and Lefort, 2015 ) and is one of the inspirations for the development of our framework. It simplifies the creation of targeted reports and the extraction of subsets of data from different domains for further analysis.…”

“…They leveraged publicly available data from the Linked Open Drug Data cloud (Samwald et al, 2011 ) to federated querying for type 2 diabetes patients. Following the same principle, Leroux and Lefort ( 2015 ) showed an efficient approach to enrich the semantics in clinical trials. They developed a semantic, linked data model from CDISC Operational Data Model 4 , focusing just on the easy data sharing and consumption, and leaving further modeling and reasoning for the future.…”

Towards achieving semantic interoperability of clinical study data with FHIR

“…ODM, however, lacks a rich-enough information model to capture the innate contextual information of the clinical study data [ 7 , 13 ]. Its relative simplicity, has impacted on its ability to advance all aspects of interoperability, limiting its support for data mapping, data types, terminology and semantic representation [ 3 ].…”

“…Extensions to ODM, such as the Clinical Data Acquisition Standards Harmonization (CDASH) [ 16 ] and the Biomedical Research Integrated Domain Group (BRIDG) [ 17 ] provide a reference model, although as stated by [ 18 ]: “ studies that use CDASH CRFs achieve semantic alignment through a shared data standard, rather than through specific semantics ”. Furthermore, there is no requirement for the CDASH model to be used within ODM [ 7 ]. Moreover, uptake of CDASH and BRIDG to provide data semantics has been limited [ 3 ].…”

“…The ability to achieve cross-study analysis also necessitates clinical studies to adopt a more streamlined data structure [ 7 ]. However, the monolithic nature of the ODM data model favours a one-dimensional traversal of the clinical data along its hierarchy of Study-Subject-StudyEvent-Form-ItemGroup-Item.…”