Back to Search Start Over

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

Authors :
Zhang XA
Yates A
Vasilevsky N
Gourdine JP
Callahan TJ
Carmody LC
Danis D
Joachimiak MP
Ravanmehr V
Pfaff ER
Champion J
Robasky K
Xu H
Fecho K
Walton NA
Zhu RL
Ramsdill J
Mungall CJ
Köhler S
Haendel MA
McDonald CJ
Vreeman DJ
Peden DB
Bennett TD
Feinstein JA
Martin B
Stefanski AL
Hunter LE
Chute CG
Robinson PN
Source :
NPJ digital medicine [NPJ Digit Med] 2019; Vol. 2. Date of Electronic Publication: 2019 May 02.
Publication Year :
2019

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.<br />Competing Interests: Competing interests: D.J.V. is the President of Blue Sky Premise, LLC and participates in the development, maintenance, and distribution of LOINC. The remaining authors declare no competing interests.

Details

Language :
English
ISSN :
2398-6352
Volume :
2
Database :
MEDLINE
Journal :
NPJ digital medicine
Publication Type :
Academic Journal
Accession number :
31119199
Full Text :
https://doi.org/10.1038/s41746-019-0110-4