@inproceedings{socrates-etal-2024-yale,
title = "{Y}ale at {``}Discharge Me!{''}: Evaluating Constrained Generation of Discharge Summaries with Unstructured and Structured Information",
author = "Socrates, Vimig and
Huang, Thomas and
Ai, Xuguang and
Fereydooni, Soraya and
Chen, Qingyu and
Taylor, R Andrew and
Chartash, David",
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-1.64",
pages = "724--730",
abstract = "In this work, we propose our top-ranking (2nd place) pipeline for the generation of discharge summary subsections as a part of the BioNLP 2024 Shared Task 2: {``}Discharge Me!{''}. We evaluate both encoder-decoder and state-of-the-art decoder-only language models on the generation of two key sections of the discharge summary. To evaluate the ability of NLP methods to further alleviate the documentation burden on physicians, we also design a novel pipeline to generate the brief hospital course directly from structured information found in the EHR. Finally, we evaluate a constrained beam search approach to inject external knowledge about relevant patient problems into the text generation process. We find that a BioBART model fine-tuned on a larger fraction of the data without constrained beam search outperforms all other models.",
}
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%0 Conference Proceedings
%T Yale at “Discharge Me!”: Evaluating Constrained Generation of Discharge Summaries with Unstructured and Structured Information
%A Socrates, Vimig
%A Huang, Thomas
%A Ai, Xuguang
%A Fereydooni, Soraya
%A Chen, Qingyu
%A Taylor, R. Andrew
%A Chartash, David
%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 socrates-etal-2024-yale
%X In this work, we propose our top-ranking (2nd place) pipeline for the generation of discharge summary subsections as a part of the BioNLP 2024 Shared Task 2: “Discharge Me!”. We evaluate both encoder-decoder and state-of-the-art decoder-only language models on the generation of two key sections of the discharge summary. To evaluate the ability of NLP methods to further alleviate the documentation burden on physicians, we also design a novel pipeline to generate the brief hospital course directly from structured information found in the EHR. Finally, we evaluate a constrained beam search approach to inject external knowledge about relevant patient problems into the text generation process. We find that a BioBART model fine-tuned on a larger fraction of the data without constrained beam search outperforms all other models.
%U https://aclanthology.org/2024.bionlp-1.64
%P 724-730
Markdown (Informal)
[Yale at “Discharge Me!”: Evaluating Constrained Generation of Discharge Summaries with Unstructured and Structured Information](https://aclanthology.org/2024.bionlp-1.64) (Socrates et al., BioNLP-WS 2024)
ACL