A Neural Embedding Approach to Mapping Health Concepts to Concept Unique Identifiers...20th World Congress on Medical and Health Informatics (MEDINFO), August 9-13, 2025, Taipei, Taiwan
Understanding health concepts in free text is an important task in biomedical NLP. Being able to map the extracted concepts to unique concept identifiers can facilitate integration of and interoperability across biomedical informatics applications. Advancement of pretrained large language models mad...
| Publicado en: | Studies in Health Technology & Informatics Vol. 329; pp. 764 - 769 |
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| Autores principales: | , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
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=187334961&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187334961 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: 329 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 187334961 187334961 187334961 10.3233/SHTI250943 187334961 ppf: 764 ppct: 5 formats: tig: atl: A Neural Embedding Approach to Mapping Health Concepts to Concept Unique Identifiers...20th World Congress on Medical and Health Informatics (MEDINFO), August 9-13, 2025, Taipei, Taiwan aug: au: Keyuan JIANG BERNARD, Gordon R. affil: Purdue University Northwest sug: subj: Natural Language Processing Subject Headings Health Information Systems Congresses and Conferences Taiwan Taiwan ab: Understanding health concepts in free text is an important task in biomedical NLP. Being able to map the extracted concepts to unique concept identifiers can facilitate integration of and interoperability across biomedical informatics applications. Advancement of pretrained large language models made it possible to identify health concepts in free text with a high degree of accuracy. However, they lack the ability to map the concepts to unique identifiers correctly. In this study we investigated a neural embedding approach to mapping health concepts to the Unified Medical Language System (UMLS) Metathesaurus' Concept Unique Identifier (CUIs). A vector store containing the embeddings of 57819 unique concepts and corresponding CUIs was created, and a collection of annotated COVID-19 signs and symptoms was tested on 3 combinations of neural embeddings and vector stores. The results show that the neural embedding approach does significantly outperform the baseline string match method by >200%, which is very encouraging. However, its performance will need to be further improved for integration with large language models (LLMs). pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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