| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-06-30 |
| タイトル |
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タイトル |
Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs |
| 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 |
矢田, 竣太郎
Nishiyama, Tomohiro
若宮, 翔子
Kawazoe, Yoshimasa
Imai, Shungo
Hori, Satoko
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 |
Real-world data (RWD) in the medical field, such as electronic health records (EHRs) and medication orders, are receiving increasing attention from researchers and practitioners. While structured data have played a vital role thus far, unstructured data represented by text (e.g., discharge summaries) are not effectively utilized because of the difficulty in extracting medical information. We evaluated the information gained by supplementing structured data with clinical concepts extracted from unstructured text by leveraging natural language processing techniques. Using a machine learning-based pretrained named entity recognition tool, we extracted disease and medication names from real discharge summaries in a Japanese hospital and linked them to medical concepts using medical term dictionaries. By comparing the diseases and medications mentioned in the text with medical codes in tabular diagnosis records, we found that: (1) the text data contained richer information on patient symptoms than tabular diagnosis records, whereas the medication-order table stored more injection data than text. In addition, (2) extractable information regarding specific diseases showed surprisingly small intersections among text, diagnosis records, and medication orders. Text data can thus be a useful supplement for RWD mining, which is further demonstrated by (3) our practical application system for drug safety evaluation, which exhaustively visualizes suspicious adverse drug effects caused by the simultaneous use of anticancer drug pairs. We conclude that proper use of textual information extraction can lead to better outcomes in medical RWD mining. |
| 書誌情報 |
en : PLOS ONE
巻 19,
号 9,
発行日 2024-09-11
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| 出版者 |
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出版者 |
Public Library of Science |
| 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 |
1932-6203 |
| 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/10.1371/journal.pone.0310432 |
| 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://journals.plos.org/plosone/article?id=10.1371/journal.pone.0310432 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
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権利情報 |
$00A9 2024 Yada et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| 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 |
JapanScience and Technology Agency (JST) |
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研究課題番号 |
JPMJCR22N1 |
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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 |
