| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-07-02 |
| タイトル |
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タイトル |
Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
natural language processing |
| キーワード |
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主題Scheme |
Other |
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主題 |
named entity recognition |
| キーワード |
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主題Scheme |
Other |
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主題 |
adverse drug reaction |
| キーワード |
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主題Scheme |
Other |
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主題 |
adverse event |
| キーワード |
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主題Scheme |
Other |
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主題 |
peripheral neuropathy |
| キーワード |
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主題Scheme |
Other |
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主題 |
NLP |
| キーワード |
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主題Scheme |
Other |
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主題 |
symptoms |
| キーワード |
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主題Scheme |
Other |
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主題 |
symptom |
| キーワード |
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主題Scheme |
Other |
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主題 |
machine learning |
| キーワード |
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主題Scheme |
Other |
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主題 |
ML |
| キーワード |
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主題Scheme |
Other |
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主題 |
drug |
| キーワード |
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主題Scheme |
Other |
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主題 |
drugs |
| キーワード |
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主題Scheme |
Other |
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主題 |
pharmacology |
| キーワード |
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主題Scheme |
Other |
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主題 |
pharmacotherapy |
| キーワード |
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主題Scheme |
Other |
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主題 |
pharmaceutic |
| キーワード |
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主題Scheme |
Other |
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主題 |
pharmaceutics |
| キーワード |
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主題Scheme |
Other |
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主題 |
pharmaceuticals |
| キーワード |
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主題Scheme |
Other |
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主題 |
pharmaceutical |
| キーワード |
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主題Scheme |
Other |
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主題 |
medication |
| キーワード |
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主題Scheme |
Other |
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主題 |
medications |
| キーワード |
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主題Scheme |
Other |
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主題 |
adverse |
| キーワード |
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主題Scheme |
Other |
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主題 |
neuropathy |
| キーワード |
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主題Scheme |
Other |
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主題 |
cancer |
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主題Scheme |
Other |
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主題 |
oncology |
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主題Scheme |
Other |
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主題 |
text |
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主題Scheme |
Other |
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主題 |
texts |
| キーワード |
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主題Scheme |
Other |
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主題 |
textual |
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主題Scheme |
Other |
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主題 |
note |
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主題Scheme |
Other |
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主題 |
notes |
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主題Scheme |
Other |
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主題 |
report |
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主題Scheme |
Other |
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主題 |
reports |
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主題Scheme |
Other |
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主題 |
EHR |
| キーワード |
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主題Scheme |
Other |
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主題 |
EHRs |
| キーワード |
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主題Scheme |
Other |
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主題 |
record |
| キーワード |
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主題Scheme |
Other |
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主題 |
records |
| キーワード |
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主題Scheme |
Other |
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主題 |
detect |
| キーワード |
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主題Scheme |
Other |
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主題 |
detection |
| キーワード |
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主題Scheme |
Other |
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主題 |
detecting |
| 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 |
Nishiyama, Tomohiro
Yamaguchi, Ayane
Han, Peitao
Pereira, Lis Weiji Kanashiro
Otsuki, Yuka
Andrade, Gabriel Herman Bernardim
Kudo, Noriko
矢田, 竣太郎
若宮, 翔子
荒牧, 英治
Takada, Masahiro
Toi, Masakazu
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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 |
Background: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient’s status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Objective: This study aimed to investigate the system’s performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system. Methods: We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration. Results: Our system underestimates by 13.3 percentage points (74.0%$221260.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician’s progress notes, followed by the pharmacist’s and nursing records. Conclusions: Considering the inherent cost that requires constant monitoring of the patient’s condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents. |
| 書誌情報 |
en : JMIR Medical Informatics
巻 12,
発行日 2024-09-24
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| 出版者 |
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出版者 |
JMIR Publications |
| 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 |
2291-9694 |
| 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/10.2196/58977 |
| 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://medinform.jmir.org/2024/1/e58977/ |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
$00A9Tomohiro Nishiyama, Ayane Yamaguchi, Peitao Han, Lis Weiji Kanashiro Pereira, Yuka Otsuki, Gabriel Herman Bernardim Andrade, Noriko Kudo, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki, Masahiro Takada, Masakazu Toi. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 24.09.2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included. |
| 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 |
Japan Science and Technology Agency (JST) |
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研究課題番号 |
JPMJCR20G9 |
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研究課題名 |
医薬品安全性監視のための言語を超えた知識強化情報抽出 |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
JP21H03170 |
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研究課題名 |
ソーシャルメディアからの患者の悩み・実践知の抽出技術と活用基盤の確立 |
| 助成情報 |
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助成機関名 |
Cabinet Office, Government of Japan |
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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) |
