Classifier Chains for LOINC Transcoding.

Autor:	Michel-Picque T; LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France.; Onaos, Montpellier, France., Bringay S; LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France.; AMIS, Paul-Valery University, Montpellier, France., Poncelet P; LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France., Patel N; AMIS, Paul-Valery University, Montpellier, France.; Onaos, Montpellier, France., Mayoral G; Onaos, Montpellier, France.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2024 Aug 22; Vol. 316, pp. 1314-1318.
DOI:	10.3233/SHTI240654
Abstrakt:	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.
Databáze:	MEDLINE
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