| Autor: |
Naji, Mouncef, Masmoudi, Maroua, Zghal, Hajer Baazaoui |
| Zdroj: |
Procedia Computer Science; 2024, Vol. 246, p3694-3701, 8p |
| Abstrakt: |
The question of informed and voluntary consent emerges as a matter of significance in healthcare. Obtaining informed consent, encounters many obstacles coupled with systemic, clinician-related, and patient-related factors, demanding interventions at different levels. This paper introduces a novel approach to present personalized consent based on Large Language Models (LLMs). The personalization of information is displayed through the combination of the LLM with a knowledge graph. We focus in our approach on how the knowledge graph enhances and personalize content generation, allowing therefore the acquisition of informed consent. The paper focuses as well on aspects related to hyper-parameters of information retrieval that help giving better prompt to the LLM. Experiments have showcased intresting results in terms of personalization and information retrieval using metrics of Rouge, Faithfulness and Relevance. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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