Classifier Chains for LOINC Transcoding...Medical Informatics Europe (MIE) 34th Conference, August 25-29, 2024, Athens, Greece.
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...
| Published in: | Studies in Health Technology & Informatics Vol. 316; pp. 1314 - 1319 |
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| Main Authors: | , , , , |
| Format: | proceedings research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179286481&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179286481 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2024 vid: 316 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179286481 179286481 179286481 10.3233/SHTI240654 179286481 ppf: 1314 ppct: 5 formats: tig: atl: Classifier Chains for LOINC Transcoding...Medical Informatics Europe (MIE) 34th Conference, August 25-29, 2024, Athens, Greece. aug: au: MICHEL-PICQUE, Théodore BRINGAY, Sandra PONCELET, Pascal PATEL, Namrata MAYORAL, Guilhem affil: LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France. sug: subj: Electronic Health Records Electronic Data Interchange Data Management Methods Health Informatics Logical Observation Identifiers, Names and Codes Utilization Vocabulary Congresses and Conferences Greece Greece Human France Natural Language Processing Machine Learning Reproducibility of Results Prediction Models Validity ab: 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. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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