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The Evaluation of Medical Terms Complexity Using Lexical Features and Large Language Models

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Understanding medical terminology is critical for effective patient-doctor communication, yet many patients struggle with complex jargon. This study compares Machine Learning (ML) models and Large Language Models (LLMs) in predicting medical term complexity as a means of improving doctor-patient communication. Using survey data from 252 participants rating 1,000 words along with various lexical features, we measured the accuracy of both model types. The results show that LLMs outperform traditional lexical-feature-based models, suggesting their potential to identify complex medical terms and lay the groundwork for personalised patient-doctor communication.

Original languageEnglish
Title of host publicationProceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, RANLP 2025
EditorsGalia Angelova, Maria Kunilovskaya, Marie Escribe, Ruslan Mitkov
PublisherIncoma Ltd
Pages682-693
Number of pages12
ISBN (Electronic)9789544520984
DOIs
Publication statusPublished - 2025
Event15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, RANLP 2025 - Varna, Bulgaria
Duration: 8 Sep 202510 Sep 2025

Publication series

NameInternational Conference Recent Advances in Natural Language Processing, RANLP
ISSN (Print)1313-8502

Conference

Conference15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, RANLP 2025
Country/TerritoryBulgaria
CityVarna
Period8/09/2510/09/25

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