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Assessing domain adaptation in adverse drug event extraction on real-world breast cancer records
http://hdl.handle.net/10061/0002000776
http://hdl.handle.net/10061/00020007765e6f17f3-9283-4444-943b-62fe75cbe2e6
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||||||
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| 公開日 | 2025-02-14 | |||||||||||||||||
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| タイトル | Assessing domain adaptation in adverse drug event extraction on real-world breast cancer records | |||||||||||||||||
| 言語 | ||||||||||||||||||
| 言語 | eng | |||||||||||||||||
| キーワード | ||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||
| 主題 | Natural language processing | |||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||
| 主題 | Breast cancer | |||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||
| 主題 | Adverse drug event | |||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||
| 主題 | Electronic health record | |||||||||||||||||
| 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 |
Herman Bernardim Andrade, Gabriel
× Herman Bernardim Andrade, Gabriel
× Nishiyama, Tomohiro
× Fujimaki, Takako
× 矢田, 竣太郎× 若宮, 翔子× Takagi, Mari
× Kato, Mizuki
× Miyashiro, Isao
× 荒牧, 英治 |
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| 抄録 | ||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||
| 内容記述 | Background: Adverse Drug Events (ADE) are key information present in unstructured portions of Electronic Health Records. These pose a significant challenge in healthcare, ranging from mild discomfort to severe complications, and can impact patient safety and treatment outcomes. Methods: We explore the influence of domain shift between a set of dummy clinical notes and a real-world hospital corpus of Japanese clinical notes of breast cancer treatment when extracting ADEs from free text. We annotated a subset of the hospital dataset and used it to fine-tune a Named Entity Recognition (NER) model, initially trained with the set of dummy documents. We used increasing amounts of the annotated data and evaluated the impact on the model's performance. Additionally, we examined the extracted information to identify combinations of drugs that are likely to cause ADEs. Results: We show that domain adaptation can significantly improve model performance in the new domain, as by feeding a small subset of 100 documents for the fine-tuning process we saw a 40% improvement in model performance. However, we also noticed diminishing returns when fine-tuning the model with a larger dataset. For instance, by feeding eight times more data, we only saw further 18% improvement in extraction performance. Conclusion: While variations in writing style and vocabulary in clinical corpora can significantly impact the quality of NER results. We show that domain adaptation can be of great aid in mitigating these discrepancies and achieving better performance. Yet, while providing in-domain data to a model helps, there are diminishing returns when fine-tuning with large amounts of data. |
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| 書誌情報 |
en : International Journal of Medical Informatics 巻 191, 発行日 2024-11-01 |
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| 出版者 | Elsevier | |||||||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||||||
| 収録物識別子 | 1872-8243 | |||||||||||||||||
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| 関連タイプ | isReplacedBy | |||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||
| 関連識別子 | https://doi.org/10.1016/j.ijmedinf.2024.105539 | |||||||||||||||||
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| 関連タイプ | isReplacedBy | |||||||||||||||||
| 識別子タイプ | URI | |||||||||||||||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S1386505624002028?via%3Dihub | |||||||||||||||||
| 権利 | ||||||||||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||||||||
| 権利情報 | $00A9 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). | |||||||||||||||||
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| 出版タイプ | NA | |||||||||||||||||
