Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database.
Journal:
Journal of the American Medical Informatics Association : JAMIA
PMID:
30137378
Abstract
OBJECTIVE: Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes.