4.7 Article

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

期刊

NPJ DIGITAL MEDICINE
卷 2, 期 -, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41746-019-0110-4

关键词

-

资金

  1. National Institutes of Health's National Center for Advancing Translational Sciences [U24TR00230]
  2. Biomedical Data Translator program [OT3TR002019, OT3TR002020]
  3. Clinical and Translational Science program [UL1TR002489, UL1TR002369]
  4. Intramural Research Program within the National Library of Medicine, National Institutes of Health
  5. National Human Genome Research Institute, National Institutes of Health [NR24OD011883]
  6. U.S. National Library of Medicine [HHSN276201400008C]
  7. Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health [K23HD091295]
  8. National Institute of Health [R01LM008111]
  9. Colorado Biomedical Informatics Training Program [T15LM009451]
  10. EPA Cooperative Agreement [CR 83578501]
  11. NATIONAL LIBRARY OF MEDICINE [ZIALM008925] Funding Source: NIH RePORTER

向作者/读者索取更多资源

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINCencoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据