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

Xingmin Aaron Zhang, Amy Yates, Nicole Vasilevsky, J. P. Gourdine, Tiffany J. Callahan, Leigh C. Carmody, Daniel Danis, Marcin P. Joachimiak, Vida Ravanmehr, Emily R. Pfaff, James Champion, Kimberly Robasky, Hao Xu, Karamarie Fecho, Nephi A. Walton, Richard L. Zhu, Justin Ramsdill, Christopher J. Mungall, Sebastian Köhler, Melissa A. HaendelClement J. McDonald, Daniel J. Vreeman, David B. Peden, Tellen D. Bennett, James A. Feinstein, Blake Martin, Adrianne L. Stefanski, Lawrence E. Hunter, Christopher G. Chute, Peter N. Robinson

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

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 LOINC-encoded 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.

Original languageEnglish (US)
Article number32
Journalnpj Digital Medicine
Volume2
Issue number1
DOIs
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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