RT Journal Article
SR Electronic
T1 Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 519231
DO 10.1101/519231
A1 Xingmin Aaron Zhang
A1 Amy Yates
A1 Nicole Vasilevsky
A1 JP Gourdine
A1 Leigh C. Carmody
A1 Daniel Danis
A1 Marcin P. Joachimiak
A1 Vida Ravanmehr
A1 Emily R. Pfaff
A1 James Champion
A1 Kimberly Robasky
A1 Hao Xu
A1 Karamarie Fecho
A1 Nephi A. Walton
A1 Richard Zhu
A1 Justin Ramsdill
A1 Chris Mungall
A1 Sebastian Köhler
A1 Melissa A. Haendel
A1 Clem McDonald
A1 Daniel J. Vreeman
A1 David B. Peden
A1 Christopher G. Chute
A1 Peter N. Robinson
YR 2019
UL http://biorxiv.org/content/early/2019/01/13/519231.abstract
AB 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 the Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2421 commonly used laboratory tests with HPO terms. Using these annotations, a software assesses laboratory test results and converts each 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 reusing readily available laboratory tests in EHR for deep phenotyping and using the hierarchical structure of HPO for association studies with medical outcomes and genomics.One Sentence Summary We present an approach to semantically integrating LOINC-encoded laboratory data with the Human Phenotype Ontology and show that the integrated LOINC data can be used to identify biomarkers for asthma from electronic health record data.