| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-10-23 |
| タイトル |
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タイトル |
Adverse event signal extraction from cancer patients’ narratives focusing on impact on their daily-life activities |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Nishioka, Satoshi
Asano, Masaki
矢田, 竣太郎
荒牧, 英治
Yajima, Hiroshi
Yanagisawa, Yuki
Sayama, Kyoko
Kizaki, Hayato
Hori, Satoko
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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 |
Adverse event (AE) management is important to improve anti-cancer treatment outcomes, but it is known that some AE signals can be missed during clinical visits. In particular, AEs that affect patients’ activities of daily living (ADL) need careful monitoring as they may require immediate medical intervention. This study aimed to build deep-learning (DL) models for extracting signals of AEs limiting ADL from patients’ narratives. The data source was blog posts written in Japanese by breast cancer patients. After pre-processing and annotation for AE signals, three DL models (BERT, ELECTRA, and T5) were trained and tested in three different approaches for AE signal identification. The performances of the trained models were evaluated in terms of precision, recall, and F1 scores. From 2,272 blog posts, 191 and 702 articles were identified as describing AEs limiting ADL or not limiting ADL, respectively. Among tested DL modes and approaches, T5 showed the best F1 scores to identify articles with AE limiting ADL or all AE: 0.557 and 0.811, respectively. The most frequent AE signals were “pain or numbness”, “fatigue” and “nausea”. Our results suggest that this AE monitoring scheme focusing on patients’ ADL has potential to reinforce current AE management provided by medical staff. |
| 書誌情報 |
en : Scientific Reports
巻 13,
号 1,
発行日 2023-09-19
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| 出版者 |
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出版者 |
Nature Research |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2045-2322 |
| 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/10.1038/s41598-023-42496-1 |
| 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://www.nature.com/articles/s41598-023-42496-1 |
| 権利 |
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権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
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権利情報 |
$00A9 The Author(s) 2023 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| 著者版フラグ |
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出版タイプ |
NA |
