Papers by Aitziber Atutxa Salazar
MedMT5: An Open-Source Multilingual Text-to-Text LLM for the Medical Domain (2024.lrec-main)
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Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata, Andrea Zaninello
| Challenge: | Existing studies on large language models for medical applications have focused on a single language . medical mT5 outperforms both encoders and similar sized text-to-text models in English, French, and Italian benchmarks . |
| Approach: | They propose to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. |
| Outcome: | The proposed model outperforms encoders and similar sized models on the Spanish, French, and Italian benchmarks while being competitive with current state-of-the-art models in English. |
A Virtual Patient Dialogue System Based on Question-Answering on Clinical Records (2024.lrec-main)
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| Challenge: | a new approach to annotating medical dialogues with intents is proposed for virtual patients . a VP is a system that allows medical students to simulate a real clinical consultation . |
| Approach: | They propose to annotate medical dialogue questions in Spanish and a second dataset of dialogues using a novel annotation approach. |
| Outcome: | The proposed approach eliminates the need for manually structured patient records . the two datasets and the code will be freely available for the research community. |
IxaMed at PharmacoNER Challenge 2019 (D19-57)
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| Challenge: | The aim of this paper is to present our approach in the PharmacoNER 2019 task. |
| Approach: | They propose to use a Bi-LSTM with a CRF to identify named entities from clinical case studies written in Spanish. |
| Outcome: | The proposed approach achieves the best score (86.81 F-Score) combining pretrained word embeddings of Wikipedia and Electronic Health Records with contextual string embedds. |