@inproceedings{boulanger-etal-2024-using,
title = "Using Structured Health Information for Controlled Generation of Clinical Cases in {F}rench",
author = {Boulanger, Hugo and
Hiebel, Nicolas and
Ferret, Olivier and
Fort, Kar{\"e}n and
N{\'e}v{\'e}ol, Aur{\'e}lie},
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Bitterman, Danielle",
booktitle = "Proceedings of the 6th Clinical Natural Language Processing Workshop",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clinicalnlp-1.14",
doi = "10.18653/v1/2024.clinicalnlp-1.14",
pages = "172--184",
abstract = "Text generation opens up new prospects for overcoming the lack of open corpora in fields such as healthcare, where data sharing is bound by confidentiality. In this study, we compare the performance of encoder-decoder and decoder-only language models for the controlled generation of clinical cases in French. To do so, we fine-tuned several pre-trained models on French clinical cases for each architecture and generate clinical cases conditioned by patient demographic information (gender and age) and clinical features.Our results suggest that encoder-decoder models are easier to control than decoder-only models, but more costly to train.",
}
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<abstract>Text generation opens up new prospects for overcoming the lack of open corpora in fields such as healthcare, where data sharing is bound by confidentiality. In this study, we compare the performance of encoder-decoder and decoder-only language models for the controlled generation of clinical cases in French. To do so, we fine-tuned several pre-trained models on French clinical cases for each architecture and generate clinical cases conditioned by patient demographic information (gender and age) and clinical features.Our results suggest that encoder-decoder models are easier to control than decoder-only models, but more costly to train.</abstract>
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%0 Conference Proceedings
%T Using Structured Health Information for Controlled Generation of Clinical Cases in French
%A Boulanger, Hugo
%A Hiebel, Nicolas
%A Ferret, Olivier
%A Fort, Karën
%A Névéol, Aurélie
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Bitterman, Danielle
%S Proceedings of the 6th Clinical Natural Language Processing Workshop
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F boulanger-etal-2024-using
%X Text generation opens up new prospects for overcoming the lack of open corpora in fields such as healthcare, where data sharing is bound by confidentiality. In this study, we compare the performance of encoder-decoder and decoder-only language models for the controlled generation of clinical cases in French. To do so, we fine-tuned several pre-trained models on French clinical cases for each architecture and generate clinical cases conditioned by patient demographic information (gender and age) and clinical features.Our results suggest that encoder-decoder models are easier to control than decoder-only models, but more costly to train.
%R 10.18653/v1/2024.clinicalnlp-1.14
%U https://aclanthology.org/2024.clinicalnlp-1.14
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.14
%P 172-184
Markdown (Informal)
[Using Structured Health Information for Controlled Generation of Clinical Cases in French](https://aclanthology.org/2024.clinicalnlp-1.14) (Boulanger et al., ClinicalNLP-WS 2024)
ACL