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

• Published in: NPJ digital medicine Vol. 2; no. 1
• Main Authors: Zhang, Xingmin Aaron, Yates, Amy, Vasilevsky, Nicole, Gourdine, J. P., Callahan, Tiffany J., Carmody, Leigh C., Danis, Daniel, Joachimiak, Marcin P., Ravanmehr, Vida, Pfaff, Emily R., Champion, James, Robasky, Kimberly, Xu, Hao, Fecho, Karamarie, Walton, Nephi A., Zhu, Richard L., Ramsdill, Justin, Mungall, Christopher J., Köhler, Sebastian, Haendel, Melissa A., McDonald, Clement J., Vreeman, Daniel J., Peden, David B., Bennett, Tellen D., Feinstein, James A., Martin, Blake, Stefanski, Adrianne L., Hunter, Lawrence E., Chute, Christopher G., Robinson, Peter N.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.05.2019; Nature Publishing Group; Springer Nature
• Subjects: 60 APPLIED LIFE SCIENCES; 692/308/53/2423; 692/308/575; 692/699/1785/31; asthma; Biomarkers; Biomedicine; Biotechnology; Digital technology; Electronic health records; Health informatics; Laboratories; Medicine; Medicine & Public Health; predictive markers; translational research
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-019-0110-4

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.