Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database

Abstract

Using a completely automated process, we are able to assign LOINC codes to unlabeled data with high accuracy. When the model-predicted LOINC code differed from the original LOINC code, the model prediction was correct in the vast majority of cases. This scalable, automated algorithm may improve data quality and interoperability, while substantially reducing the manual effort currently needed to accurately map laboratory data.

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