Classifier Chains for LOINC Transcoding.

PURPOSE:Mapping clinical observations and medical test results into the standardized vocabulary LOINC is a prerequisite for exchanging clinical data between health information systems and ensuring efficient interoperability. METHODS:We present a comparison of three approaches for LOINC transcoding applied to French data collected from real-world settings. These approaches include both a state-of-the-art language model approach and a classifier chains approach. RESULTS:Our study demonstrates that we successfully improve the performance of the baselines using the classifier chains approach and compete effectively with state-of-the-art language models. CONCLUSIONS:Our approach proves to be efficient, cost-effective despite reproducibility challenges and potential for future optimizations and dataset testing.