Abstract
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.
Highlights
Clinical Data Searches Through Knowledge Level QueriesWhile secondary use of electronic medical records (EMRs) are widely expected [1,2], medical data in general do not contain adequate amounts of information or knowledge in their original format, making it difficult to retrieve the desired data based on the knowledge in the clinical domain
We present results of a benchmark that measures the execution time to show that the searches over resource description framework (RDF)-ized Health Level Seven ICD10 (HL7) messages through SPARQL provide a feasible response speed
The Query 2 expression of "drug types having leukopenia as adverse events" yielded 131 types of drug codes using SIDER 2, and the Query 3 expression of "of the atypical antipsychotic drugs, those having a serotonin 2C adverse drug events (ADEs) (5HT2C) or histamine antagonist of the H1 receptor HL7 Application Programming Interface (HAPI) (H1) inhibitory effect" yielded 78 drug types, with Queries 2 and 3 obtaining 1171 and 58 results, respectively
Summary
Clinical Data Searches Through Knowledge Level QueriesWhile secondary use of electronic medical records (EMRs) are widely expected [1,2], medical data in general do not contain adequate amounts of information or knowledge in their original format, making it difficult to retrieve the desired data based on the knowledge in the clinical domain. As a similar example, when we try to screen patients with medication history of "drugs that cause leucopenia," rather than having to list in a query hundreds of codes for drugs showing the adverse events, if drugs that cause leukopenia are identified using external knowledge resources, and if a search is performed over medication data based on the identified drugs, it would facilitate the research use of EMRs. Clinical Data Searches Using Life Science Knowledge Resources. We converted five kinds of drug databases into RDF and provided explicit links between the corresponding items among them With those linked drug data, we developed a method for query expansion to search the clinical data using semantic information on drug classes along with four types of temporal patterns. 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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