Translating Natural Language Questions into SNOMED Expression Constraint Language...Health Innovation Community, August 18-20, 2025, Melbourne Convention and Exhibition Centre, Melbourne, Australia.
The SNOMED Expression Constraint Language (ECL) is a powerful but complex tool for querying clinical concepts within SNOMED CT, playing a critical role in clinical decision support, data analysis, and healthcare interoperability. However, its steep learning curve - requiring both syntactic expertise...
| Publicado en: | Studies in Health Technology & Informatics Vol. 333; pp. 58 - 64 |
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| Autor principal: | |
| Formato: | proceedings research tables/charts Journal Article |
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Sage Publications Inc.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189355126&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189355126 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 333 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 189355126 189355126 189355126 10.3233/SHTI251576 189355126 ppf: 58 ppct: 6 formats: tig: atl: Translating Natural Language Questions into SNOMED Expression Constraint Language...Health Innovation Community, August 18-20, 2025, Melbourne Convention and Exhibition Centre, Melbourne, Australia. aug: au: Hoa NGO affil: The Australian E-Health Research Centre, CSIRO sug: subj: Translations Natural Language Processing Snomed Nomenclature Deep Learning Programming Languages Decision Support Systems, Clinical Artificial Intelligence Human Congresses and Conferences Australia Australia ab: The SNOMED Expression Constraint Language (ECL) is a powerful but complex tool for querying clinical concepts within SNOMED CT, playing a critical role in clinical decision support, data analysis, and healthcare interoperability. However, its steep learning curve - requiring both syntactic expertise and in-depth knowledge of SNOMED CT's coding system - creates significant challenges for non-specialists. To address this issue, a novel approach is proposed that leverages state-of-the-art Large Language Models (LLMs) to translate natural language questions into ECL queries. The model is designed to perform bidirectional tasks: generating ECL queries from user questions and providing natural language explanations for ECL queries, making the language more accessible and easier to understand. This work represents the first research effort to tackle this specific translation challenge, supported by the development of custom datasets and a novel pipeline that integrates multiple AI agents. Evaluation results demonstrate that the proposed model achieves 83.78% accuracy, highlighting the significant potential of LLMs for translating natural language questions into SNOMED ECL. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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