@inproceedings{koontz-etal-2024-ixa,
title = "Ixa-{M}ed at Discharge Me! Retrieval-Assisted Generation for Streamlining Discharge Documentation",
author = "Koontz, Jordan C. and
Oronoz, Maite and
P{\'e}rez, Alicia",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.57",
pages = "658--663",
abstract = "In this paper we present our system for the BioNLP ACL{'}24 {``}Discharge Me!{''} task on automating discharge summary section generation. Using Retrieval-Augmented Generation, we combine a Large Language Model (LLM) with external knowledge to guide the generation of the target sections. Our approach generates structured patient summaries from discharge notes using an instructed LLM, retrieves relevant {``}Brief Hospital Course{''} and {``}Discharge Instructions{''} examples via BM25 and SentenceBERT, and provides this context to a frozen LLM for generation. Our top system using SentenceBERT retrieval achieves an overall score of 0.183, outperforming zero-shot baselines. We analyze performance across different aspects, discussing limitations and future research directions.",
}
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<abstract>In this paper we present our system for the BioNLP ACL’24 “Discharge Me!” task on automating discharge summary section generation. Using Retrieval-Augmented Generation, we combine a Large Language Model (LLM) with external knowledge to guide the generation of the target sections. Our approach generates structured patient summaries from discharge notes using an instructed LLM, retrieves relevant “Brief Hospital Course” and “Discharge Instructions” examples via BM25 and SentenceBERT, and provides this context to a frozen LLM for generation. Our top system using SentenceBERT retrieval achieves an overall score of 0.183, outperforming zero-shot baselines. We analyze performance across different aspects, discussing limitations and future research directions.</abstract>
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%0 Conference Proceedings
%T Ixa-Med at Discharge Me! Retrieval-Assisted Generation for Streamlining Discharge Documentation
%A Koontz, Jordan C.
%A Oronoz, Maite
%A Pérez, Alicia
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F koontz-etal-2024-ixa
%X In this paper we present our system for the BioNLP ACL’24 “Discharge Me!” task on automating discharge summary section generation. Using Retrieval-Augmented Generation, we combine a Large Language Model (LLM) with external knowledge to guide the generation of the target sections. Our approach generates structured patient summaries from discharge notes using an instructed LLM, retrieves relevant “Brief Hospital Course” and “Discharge Instructions” examples via BM25 and SentenceBERT, and provides this context to a frozen LLM for generation. Our top system using SentenceBERT retrieval achieves an overall score of 0.183, outperforming zero-shot baselines. We analyze performance across different aspects, discussing limitations and future research directions.
%U https://aclanthology.org/2024.bionlp-1.57
%P 658-663
Markdown (Informal)
[Ixa-Med at Discharge Me! Retrieval-Assisted Generation for Streamlining Discharge Documentation](https://aclanthology.org/2024.bionlp-1.57) (Koontz et al., BioNLP-WS 2024)
ACL