Using LLMs to generate patient journeys in portuguese: an experiment

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2025

Autores

Munna, Tahsir Ahmed (Autor) Outra; Fernandes, Ana Luísa (Autor) Outra; Silvano, Purificação (Autor) FLUP; Guimarães, Nuno (Autor) FCUP; Jorge, Alípio (Autor) FCUP

Ata de Conferência Internacional

Título: Proceedings of Text2Story - Eighth Workshop on Narrative Extraction From Texts held in conjunction with the 47th European Conference on Information Retrieval (ECIR 2025)

Text2Story — Eighth Workshop on Narrative Extraction From Texts held in conjunction with the 47th European Conference on Information Retrieval (ECIR 2025)

Lucca, 2025

Classificação Científica

CORDIS: Humanidades > Ciências da linguagem > Linguística

Outras Informações

Abstract (EN): The relationship of a patient with a hospital from admission to discharge is often kept in a series of textual documents that describe the patient’s journey. These documents are important to analyze the di"erent steps of the clinical process and to make aggregated studies of the paths of patients in the hospital. In this paper, we explore the potential of Large Language Models (LLMs) to generate realistic and comprehensive patient journeys in European Portuguese, addressing the scarcity of medical data in this speci!c context. We employed Google’s Gemini 1.5 Flash model and utilized a dataset of 285 European Portuguese published case reports from the SPMI website, published by the Portuguese Society of Internal Medicine, as references for generating synthetic medical reports. Our methodology involves a sequential approach to generating a synthetic patient journey. Initially, we generate an admission report, followed by a discharge report. Subsequently, we generate a comprehensive patient journey that integrates the admission, multiple daily progress reports, and the discharge into a cohesive narrative. This end-to-end process ensures a realistic and detailed representation of the patient’s clinical pathway as a patient’s journey. The generated reports were rigorously evaluated by medical and linguistic professionals, as well as automatic metrics to measure the inclusion of key medical entities, similarity to the case report, and correct Portuguese variant. Both qualitative and quantitative evaluations con!rmed that the generated synthetic reports are predominantly written in European Portuguese without the loss of important medical information from the case reports. This work contributes to developing high-quality synthetic medical data for training LLMs and advancing AI-driven healthcare applications in under-resourced language settings.12

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12