LOINC2HPO: Curation of Phenotype Data from the Electronic Health Records using the Human Phenotype Ontology
Year: 2019
Type: other
Abstract: Talk at the International Society for Biocuration Conference 2019, Cambridge, UK. Electronic Health Record (EHR) data are often encoded using Logical Observation Identifier Names and Codes (LOINC) in the United States, which is a universal standard for coding medical laboratory observations in EHRs. LOINC encoded clinical tests can be inferred as phenotypic outcomes, and offer the potential for secondary reuse of EHR data for patient phenotyping. The Human Phenotype Ontology (HPO) has been widely used for deep phenotyping in research and for diagnostic purposes. It contains over 13,500 classes that represent phenotypic abnormalities encountered in human diseases. Mapping the LOINC codes to HPO terms provides an opportunity to structure this phenotypic information and enable automatic extraction of detailed deep phenotypic profiles of laboratory results for downstream studies. Multiple LOINC codes can code for lab tests that can be represented as a single phenotype; for example multiple codes exist for measurements of nitrate levels in urine, which could be interpreted as HP:0031812 Nitrituria. To harmonize lab test data from EHRs to HPO, we developed a curation tool that converts EHR observations into HPO terms. To date, over 2400 LOINC codes have been mapped to HPO terms and our mapping library is freely available online . To demonstrate the utility of these mapped codes, we performed a pilot study with de-identified data from asthma patient’s health records. We were able to convert 83% of real-world laboratory tests into HPO-encoded phenotypes. Analysis of the LOINC2HPO-encoded data showed known and new phenotypes were are enriched in asthma patients such as eosinophilia and several other abnormal laboratory measurements. This preliminary evidence suggests that LOINC data converted to HPO can be used for machine learning approaches to support genomic phenotype-driven diagnostics for rare disease patients, and to perform EHR based mechanistic research.
Authors: Nicole Vasilevsky, Aaron Zhang, Amy Yates, JP Gourdine, Tiffany Callahan, Melissa Haendel, Larry Hunter, Peter Robinson
Institution: Oregon Health & Science University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4416517170