FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model.
FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model.
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DOI：
10.1016/j.jbi.2022.104201
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发表时间：
2022-10
影响因子：
4.5
通讯作者：
Jiang, Guoqian
中科院分区：
文献类型：
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作者：
Xiao, Guohui;Pfaff, Emily;Prud'hommeaux, Eric;Booth, David;Sharma, Deepak K;Huo, Nan;Yu, Yue;Zong, Nansu;Ruddy, Kathryn J;Chute, Christopher G;Jiang, Guoqian
知识图（KGs）在医疗保健中实现可解释的人工智能（AI）应用方面发挥着关键作用。针对异构电子健康记录（EHR）构建临床知识图（CKG）一直是研究和医疗保健AI社区所期望的。从标准化的角度来看，基于社区的标准，如快速医疗互操作性资源（FHIR）和观察性医疗成果伙伴关系（OMOP）公共数据模型（CDM）越来越多地用于表示和标准化临床数据分析的EHR数据，然而，这种标准在构建CKG方面的潜力尚未得到很好的研究。开发和评估将OMOP基于CDM的临床数据存储库暴露于符合FHIR资源描述框架（RDF）规范的虚拟临床KG的方法和工具。我们开发了一个名为FHIR-Ontop-OMOP的系统，从OMOP关系数据库中生成虚拟临床KG。我们利用基于OMOP CDM的重症监护医学信息集市（MIMIC-III）数据存储库，从数据转换的忠实性和生成的CKG与FHIR RDF规范的一致性方面评估FHIR-Ontop-OMOP系统。该系统的beta版本已经发布。该系统共实现了11个OMOP CDM临床数据、卫生系统和词汇表的100多个数据元素映射，覆盖了11个FHIR资源。MIMIC-III生成的虚拟CKG包含46，520个FHIR患者实例、716，595个条件实例、1，063，525个程序实例、24，934，751个药物声明实例、365，181，104个观察结果实例和4，779，672个可编码概念实例。通过五对SQL（通过MIMIC数据库）和SPARQL（通过虚拟CKG）查询识别的患者计数是相同的，确保了数据转换的可靠性。100例患者在RDF三元组中生成的CKG完全符合FHIR RDF规范。FHIR-Ontop-OMOP系统可以将OMOP数据库公开为符合FHIR的RDF图。它提供了一个有意义的用例，展示了FHIR和OMOP CDM之间的互操作性可以实现的潜力。在FHIR RDF中生成的临床KG为在医疗保健中实现可解释的AI应用提供了语义基础。
Knowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated. To develop and evaluate methods and tools that expose the OMOP CDM-based clinical data repositories into virtual clinical KGs that are compliant with FHIR Resource Description Framework (RDF) specification. We developed a system called FHIR-Ontop-OMOP to generate virtual clinical KGs from the OMOP relational databases. We leveraged an OMOP CDM-based Medical Information Mart for Intensive Care (MIMIC-III) data repository to evaluate the FHIR-Ontop-OMOP system in terms of the faithfulness of data transformation and the conformance of the generated CKGs to the FHIR RDF specification. A beta version of the system has been released. A total of more than 100 data element mappings from 11 OMOP CDM clinical data, health system and vocabulary tables were implemented in the system, covering 11 FHIR resources. The generated virtual CKG from MIMIC-III contains 46,520 instances of FHIR Patient, 716,595 instances of Condition, 1,063,525 instances of Procedure, 24,934,751 instances of MedicationStatement, 365,181,104 instances of Observations, and 4,779,672 instances of CodeableConcept. Patient counts identified by five pairs of SQL (over the MIMIC database) and SPARQL (over the virtual CKG) queries were identical, ensuring the faithfulness of the data transformation. Generated CKG in RDF triples for 100 patients were fully conformant with the FHIR RDF specification. The FHIR-Ontop-OMOP system can expose OMOP database as a FHIR-compliant RDF graph. It provides a meaningful use case demonstrating the potentials that can be enabled by the interoperability between FHIR and OMOP CDM. Generated clinical KGs in FHIR RDF provide a semantic foundation to enable explainable AI applications in healthcare.