Semantic querying of relational data for clinical intelligence: a semantic web services-based approach

Riazanov, Alexandre; Klein, Artjom; Shaban-Nejad, Arash; Rose, Greg; Forster, Alan J.; Buckeridge, David L.; Baker, Christopher J. O.

doi:10.1186/2041-1480-4-9

Cited by 18 publications

(12 citation statements)

References 20 publications

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“…Although we showed these queries as possible as simple, they might be typically verbose and difficult to write. It is conceivable that using Allen’s temporal predicates such as "before," "after," and "during" in the pattern matching of the query [ 14 ] is useful to avoid the SPARQL filter-based comparison of the time. In order to do that, an interval-based temporal information should be given to the comparable events and they should be connected according to their relationships when the RDF data are created.…”

Section: Discussionmentioning

confidence: 99%

“…Assélé et al [ 13 ] developed a framework to perform SPARQL Protocol and RDF Query Language (SPARQL) queries on clinical databases to obtain results about antibiotic resistance and compared their approach with existing business intelligence approaches in terms of usability and functionality. Riazanov et al [ 14 ] developed an ontology for the clinical domain and reported that SPARQL queries can be expressed and executed in an ad hoc manner by mapping the developed clinical domain ontology and clinical data. Pathak et al [ 15 , 16 ] used publicly available life science data resources as Linked Data and searched over EMR databases integrated with these resources through SPARQL federation queries.…”

Section: Introductionmentioning

confidence: 99%

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A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources

Kawazoe

Imai²

2016

JMIR Med Inform

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BackgroundHealth level seven version 2.5 (HL7 v2.5) is a widespread messaging standard for information exchange between clinical information systems. By applying Semantic Web technologies for handling HL7 v2.5 messages, it is possible to integrate large-scale clinical data with life science knowledge resources.ObjectiveShowing feasibility of a querying method over large-scale resource description framework (RDF)-ized HL7 v2.5 messages using publicly available drug databases.MethodsWe developed a method to convert HL7 v2.5 messages into the RDF. We also converted five kinds of drug databases into RDF and provided explicit links between the corresponding items among them. With those linked drug data, we then developed a method for query expansion to search the clinical data using semantic information on drug classes along with four types of temporal patterns. For evaluation purpose, medication orders and laboratory test results for a 3-year period at the University of Tokyo Hospital were used, and the query execution times were measured.ResultsApproximately 650 million RDF triples for medication orders and 790 million RDF triples for laboratory test results were converted. Taking three types of query in use cases for detecting adverse events of drugs as an example, we confirmed these queries were represented in SPARQL Protocol and RDF Query Language (SPARQL) using our methods and comparison with conventional query expressions were performed. The measurement results confirm that the query time is feasible and increases logarithmically or linearly with the amount of data and without diverging.ConclusionsThe proposed methods enabled query expressions that separate knowledge resources and clinical data, thereby suggesting the feasibility for improving the usability of clinical data by enhancing the knowledge resources. We also demonstrate that when HL7 v2.5 messages are automatically converted into RDF, searches are still possible through SPARQL without modifying the structure. As such, the proposed method benefits not only our hospitals, but also numerous hospitals that handle HL7 v2.5 messages. Our approach highlights a potential of large-scale data federation techniques to retrieve clinical information, which could be applied as applications of clinical intelligence to improve clinical practices, such as adverse drug event monitoring and cohort selection for a clinical study as well as discovering new knowledge from clinical information.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources

Kawazoe

Imai²

2016

JMIR Med Inform

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“…SADI services consume and produce RDF instances of OWL classes where the input data are decorated until they become an instance of the output OWL class. The services are deployed in a registry and can be automatically discovered, orchestrated, and invoked to return data in RDF on the query clients SHARE [ 29 ] and HYDRA [ 30 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability

Manir¹,

Brenas²,

Baker³

et al. 2018

JMIR Public Health Surveill

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BackgroundAccording to the World Health Organization, malaria surveillance is weakest in countries and regions with the highest malaria burden. A core obstacle is that the data required to perform malaria surveillance are fragmented in multiple data silos distributed across geographic regions. Furthermore, consistent integrated malaria data sources are few, and a low degree of interoperability exists between them. As a result, it is difficult to identify disease trends and to plan for effective interventions.ObjectiveWe propose the Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) platform for use in malaria surveillance based on semantic data federation. Using this approach, it is possible to access distributed data, extend and preserve interoperability between multiple dynamic distributed malaria sources, and facilitate detection of system changes that can interrupt mission-critical global surveillance activities.MethodsWe used Semantic Automated Discovery and Integration (SADI) Semantic Web Services to enable data access and improve interoperability, and the graphical user interface-enabled semantic query engine HYDRA to implement the target queries typical of malaria programs. We implemented a custom algorithm to detect changes to community-developed terminologies, data sources, and services that are core to SIEMA. This algorithm reports to a dashboard. Valet SADI is used to mitigate the impact of changes by rebuilding affected services.ResultsWe developed a prototype surveillance and change management platform from a combination of third-party tools, community-developed terminologies, and custom algorithms. We illustrated a methodology and core infrastructure to facilitate interoperable access to distributed data sources using SADI Semantic Web services. This degree of access makes it possible to implement complex queries needed by our user community with minimal technical skill. We implemented a dashboard that reports on terminology changes that can render the services inactive, jeopardizing system interoperability. Using this information, end users can control and reactively rebuild services to preserve interoperability and minimize service downtime.ConclusionsWe introduce a framework suitable for use in malaria surveillance that supports the creation of flexible surveillance queries across distributed data resources. The platform provides interoperable access to target data sources, is domain agnostic, and with updates to core terminological resources is readily transferable to other surveillance activities. A dashboard enables users to review changes to the infrastructure and invoke system updates. The platform significantly extends the range of functionalities offered by malaria information systems, beyond the state-of-the-art.

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“…Ontology-based queries When the data is stored on a structured relational database, semantic searches can be applied for answering different kinds of RQs [ 13 ]. The PONTE platform [ 14 ] enables querying on a global EHR ontology using SPARQL statements [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Querying archetype-based EHRs by search ontology-based XPath engineering

et al. 2018

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BackgroundLegacy data and new structured data can be stored in a standardized format as XML-based EHRs on XML databases. Querying documents on these databases is crucial for answering research questions. Instead of using free text searches, that lead to false positive results, the precision can be increased by constraining the search to certain parts of documents.MethodsA search ontology-based specification of queries on XML documents defines search concepts and relates them to parts in the XML document structure. Such query specification method is practically introduced and evaluated by applying concrete research questions formulated in natural language on a data collection for information retrieval purposes. The search is performed by search ontology-based XPath engineering that reuses ontologies and XML-related W3C standards.ResultsThe key result is that the specification of research questions can be supported by the usage of search ontology-based XPath engineering. A deeper recognition of entities and a semantic understanding of the content is necessary for a further improvement of precision and recall. Key limitation is that the application of the introduced process requires skills in ontology and software development. In future, the time consuming ontology development could be overcome by implementing a new clinical role: the clinical ontologist.ConclusionThe introduced Search Ontology XML extension connects Search Terms to certain parts in XML documents and enables an ontology-based definition of queries. Search ontology-based XPath engineering can support research question answering by the specification of complex XPath expressions without deep syntax knowledge about XPaths.

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Semantic querying of relational data for clinical intelligence: a semantic web services-based approach

Cited by 18 publications

References 20 publications

A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources

A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources

A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability

Querying archetype-based EHRs by search ontology-based XPath engineering

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