一个原型ETL管道,在部署纯函数以丰富知识图谱患者数据时使用HL7 FHIR RDF资源。

IF 2 3区 工程技术 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Adeel Ansari, Marisa Conte, Allen Flynn, Avanti Paturkar
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引用次数: 0

摘要

背景:对于临床护理和研究,可以通过从知识图中提取参数,然后将其作为输入,以纯函数计算新的患者特征,从而丰富具有患者数据的知识图。用新计算的患者特征丰富知识图谱的系统和透明的方法引起了人们的兴趣。当以这种方式丰富知识图中的患者数据时,现有的本体和已知的数据资源标准可以帮助促进语义互操作性。结果:我们开发并测试了一种新的数据处理管道,用于提取、计算并将新计算的结果返回到包含电子健康记录和患者调查数据的大型知识图谱。我们展示了Health Level 7的FHIR RDF工作已经指定的RDF数据资源类型可以通过编程验证,然后由这个新的数据处理管道使用,以表示新派生的患者级特征。结论:知识图技术可以通过基于标准的语义数据处理管道进行扩展,用于部署和跟踪纯函数的使用,从而从现有数据中派生出新的患者级特征。语义数据处理管道使研究企业能够通过链接的元数据报告感兴趣的新的患者级计算,这些元数据详细说明了每个新计算的起源和背景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data.

A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data.

A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data.

A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data.

Background: For clinical care and research, knowledge graphs with patient data can be enriched by extracting parameters from a knowledge graph and then using them as inputs to compute new patient features with pure functions. Systematic and transparent methods for enriching knowledge graphs with newly computed patient features are of interest. When enriching the patient data in knowledge graphs this way, existing ontologies and well-known data resource standards can help promote semantic interoperability.

Results: We developed and tested a new data processing pipeline for extracting, computing, and returning newly computed results to a large knowledge graph populated with electronic health record and patient survey data. We show that RDF data resource types already specified by Health Level 7's FHIR RDF effort can be programmatically validated and then used by this new data processing pipeline to represent newly derived patient-level features.

Conclusions: Knowledge graph technology can be augmented with standards-based semantic data processing pipelines for deploying and tracing the use of pure functions to derive new patient-level features from existing data. Semantic data processing pipelines enable research enterprises to report on new patient-level computations of interest with linked metadata that details the origin and background of every new computation.

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来源期刊
Journal of Biomedical Semantics
Journal of Biomedical Semantics MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
4.20
自引率
5.30%
发文量
28
审稿时长
30 weeks
期刊介绍: Journal of Biomedical Semantics addresses issues of semantic enrichment and semantic processing in the biomedical domain. The scope of the journal covers two main areas: Infrastructure for biomedical semantics: focusing on semantic resources and repositories, meta-data management and resource description, knowledge representation and semantic frameworks, the Biomedical Semantic Web, and semantic interoperability. Semantic mining, annotation, and analysis: focusing on approaches and applications of semantic resources; and tools for investigation, reasoning, prediction, and discoveries in biomedicine.
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