| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-09-30 |
| タイトル |
|
|
タイトル |
Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Natural Language processing |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Electronic medical records |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Drug repurposing |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Anthracycline-induced cardiotoxicity |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Adverse effects |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Chemotherapy |
| 資源タイプ |
|
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
| 著者 |
Kawazoe, Yoshimasa
Tsuchiya, Masami
Shimamoto, Kiminori
Seki, Tomohisa
Shinohara, Emiko
矢田, 竣太郎
若宮, 翔子
Imai, Shungo
荒牧, 英治
Hori, Satoko
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
In this retrospective observational study, we aimed to investigate the potential of natural language processing (NLP) for drug repositioning by analyzing the preventive effects of cardioprotective drugs against anthracycline-induced cardiotoxicity (AIC) using electronic medical records. We evaluated the effects of angiotensin II receptor blockers/angiotensin-converting enzyme inhibitors (ARB/ACEIs), beta-blockers (BBs), statins, and calcium channel blockers (CCBs) on AIC using signals extracted from clinical texts via NLP. The study included 2935 patients prescribed anthracyclines at a single hospital, with concomitant prescriptions of ARB/ACEIs, BBs, statins, and CCBs. Upon propensity score matching, groups with and without these medications were compared, and expressions suggestive of cardiotoxicity, extracted via NLP, were considered as the outcome. The hazard ratios for ARB/ACEIs, BBs, statins, and CCBs were 0.58 [95% CI: 0.38–0.88], 0.71 [95% CI: 0.35–1.44], 0.60 [95% CI 0.38–0.95], and 0.63 [95% CI: 0.45–0.88], respectively. ARB/ACEIs, statins, and CCBs significantly suppressed AIC, whereas BBs did not demonstrate statistical significance, possibly due to limited statistical power. NLP-extracted signals from clinical texts reflected the known effects of these medications, demonstrating the feasibility of NLP-based drug repositioning. Further investigation is needed to determine if similar results can be replicated using electronic medical records from other institutions. |
| 書誌情報 |
en : Scientific Reports
巻 15,
号 1,
ページ数 14,
発行日 2025-02-24
|
| 出版者 |
|
|
出版者 |
Nature Research |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2045-2322 |
| 出版者版DOI |
|
|
関連タイプ |
isReplacedBy |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1038/s41598-025-91187-6 |
| 出版者版URI |
|
|
関連タイプ |
isReplacedBy |
|
|
識別子タイプ |
URI |
|
|
関連識別子 |
https://www.nature.com/articles/s41598-025-91187-6 |
| 権利 |
|
|
権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/. |
| 著者版フラグ |
|
|
出版タイプ |
NA |
| 助成情報 |
|
|
|
助成機関名 |
Japan Science and Technology Agency (JST) |
|
|
研究課題番号 |
JPMJCR22N1 |
|
|
研究課題番号URI |
https://projectdb.jst.go.jp/grant/JST-PROJECT-22717060/ |
|
|
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 |
| 助成情報 |
|
|
|
助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
|
|
研究課題番号 |
23H03492 |
|
|
研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23K28182/ |
|
|
研究課題名 |
医療用語のエンティティリンキングに向けた実践的医療用語辞書の開発 |
| 助成情報 |
|
|
|
助成機関名 |
National Center for Global Health and Medicine (NCGM) |
|
|
研究課題番号 |
JPJ012425 |
|
|
研究課題名 |
Progress of the Next Cross-ministerial Strategic Innovation Promotion Program (SIP) on “Integrated Health Care System” |
