| アイテムタイプ |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2025-11-14 |
| タイトル |
|
|
タイトル |
Development of an Automated Classification System for Medication-Related Incident Factors: A Practical Approach to Enhancing Patient Safety Management |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
medication |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
incidents |
| 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 |
risk management |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
P-mSHELL model |
| 資源タイプ |
|
|
資源タイプ |
conference paper |
| アクセス権 |
|
|
アクセス権 |
open access |
| 著者 |
Takamatsu, Yuri
Ebara, Sayaka
Kizaki, Hayato
Watabe, Satoshi
Imai, Shungo
矢田, 竣太郎
荒牧, 英治
Yasumuro, Osamu
Funakoshi, Ryohkan
Hori, Satoko
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Analyzing medication-related incident reports is crucial for patient safety; however, systematically extracting the underlying factors contributing to incident occurrence remains challenging. We developed a multi-label classifier that automatically identified incident factors from 1,212 drug-related incident reports using the Bidirectional Encoder Representations from Transformers and its derivatives. Based on the P-mSHELL model, a comprehensive framework for incident factor analysis, we established seven distinct factor categories and evaluated various pre-trained models through five-fold cross-validation. Almost all models achieved macro F1 scores exceeding 0.6, with the lightweight A Lite BERT model showing comparable performance to BERT. This study demonstrates the practical feasibility of natural language processing techniques for systematic incident factor analysis, supporting enhanced patient safety management. |
| 書誌情報 |
en : Studies in Health Technology and Informatics
巻 329,
p. 758-763,
ページ数 6,
発行日 2025
|
| 会議情報 |
|
|
|
会議名 |
The 20th World Congress on Medical and Health Informatics (MEDINFO2025) |
|
回次 |
20 |
|
|
主催機関 |
International Medical Informatics Association |
|
|
開始年 |
2025 |
|
|
開始月 |
08 |
|
|
開始日 |
09 |
|
|
終了年 |
2025 |
|
|
終了月 |
08 |
|
|
終了日 |
13 |
|
|
開催期間 |
2025-08-09-2025-08-13 |
|
|
開催会場 |
Taipei International Convention Center |
|
|
開催地 |
Taipei, Taiwan |
|
開催国 |
TWN |
| 出版者 |
|
|
出版者 |
IOS Press |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
1879-8365 |
| 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/10.3233/SHTI250942 |
| 出版者版URI |
|
|
関連タイプ |
isReplacedBy |
|
|
識別子タイプ |
URI |
|
|
関連識別子 |
https://ebooks.iospress.nl/doi/10.3233/SHTI250942 |
| 権利 |
|
|
権利情報Resource |
https://creativecommons.org/licenses/by-nc/4.0/ |
|
権利情報 |
© 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). |
| 著者版フラグ |
|
|
出版タイプ |
NA |
| 助成情報 |
|
|
|
助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
|
|
研究課題番号 |
JP22K19657 |
|
|
研究課題番号URI |
https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-22K19657/ |
|
|
研究課題名 |
深層学習を用いたインシデント文章分析によるプロアクティブリスク管理手法の確立 |
