| アイテムタイプ |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2025-10-09 |
| タイトル |
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タイトル |
Exploring LLM Annotation for Adaptation of Clinical Information Extraction Models under Data-sharing Restrictions |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
conference paper |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Shimizu, Seiji
Hisada, Shohei
Uno, Yutaka
矢田, 竣太郎
若宮, 翔子
荒牧, 英治
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
In-hospital text data contains valuable clinical information, yet deploying fine-tuned small language models (SLMs) for information extraction remains challenging due to differences in formatting and vocabulary across institutions. Since access to the original in-hospital data (source domain) is often restricted, annotated data from the target hospital (target domain) is crucial for domain adaptation. However, clinical annotation is notoriously expensive and time-consuming, as it demands clinical and linguistic expertise. To address this issue, we leverage large language models (LLMs) to annotate the target domain data for the adaptation. We conduct experiments on four clinical information extraction tasks, including eight target domain data. Experimental results show that LLM-annotated data consistently enhances SLM performance and, with a larger number of annotated data, outperforms manual annotation in three out of four tasks. |
| 書誌情報 |
en : Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics
p. 14678-14694,
ページ数 17,
発行日 2025-07
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| 会議情報 |
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会議名 |
The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) |
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開始年 |
2025 |
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開始月 |
07 |
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開始日 |
27 |
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終了年 |
2025 |
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終了月 |
08 |
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終了日 |
01 |
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開催期間 |
2025-07-27 - 2025-08-01 |
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開催地 |
Vienna, Austria |
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開催国 |
AUT |
| 出版者 |
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出版者 |
Association for Computational Linguistics |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.18653/v1/2025.findings-acl.757 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://aclanthology.org/2025.findings-acl.757/ |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
ACL materials are Copyright © 1963–2025 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
National Center for Global Health and Medicine (NCGM) |
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研究課題番号 |
JPJ012425 |
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研究課題名 |
Cross-ministerial Strategic Innovation Promotion Program (SIP) on “Integrated Health Care System” |
| 助成情報 |
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助成機関名 |
Japan Science and Technology Agency (JST) |
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研究課題番号 |
JPMJCR22N1 |
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研究課題番号URI |
https://projectdb.jst.go.jp/grant/JST-PROJECT-22717060/ |
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研究課題名 |
リアルワールドテキスト処理の深化によるデータ駆動型探薬 |
