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Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach
http://hdl.handle.net/10061/0002000423
http://hdl.handle.net/10061/0002000423a8218a12-9334-4c89-897a-75ff355008d5
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||
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| 公開日 | 2024-05-23 | |||||||||
| タイトル | ||||||||||
| タイトル | Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | data mining | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | machine learning | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | medication noncompliance | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | natural language processing | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | pharmacovigilance | |||||||||
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| 主題Scheme | UDC | |||||||||
| 主題 | transfer learning | |||||||||
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| 主題Scheme | Other | |||||||||
| 主題 | text classification | |||||||||
| 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 |
Nishiyama, Tomohiro
× Nishiyama, Tomohiro
× 矢田, 竣太郎× 若宮, 翔子× Hori, Satoko
× 荒牧, 英治 |
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Background: Medication noncompliance is a critical issue because of the increased number of drugs sold on the web. Web-based drug distribution is difficult to control, causing problems such as drug noncompliance and abuse. The existing medication compliance surveys lack completeness because it is impossible to cover patients who do not go to the hospital or provide accurate information to their doctors, so a social media$2013based approach is being explored to collect information about drug use. Social media data, which includes information on drug usage by users, can be used to detect drug abuse and medication compliance in patients. Objective: This study aimed to assess how the structural similarity of drugs affects the efficiency of machine learning models for text classification of drug noncompliance. Methods: This study analyzed 22,022 tweets about 20 different drugs. The tweets were labeled as either noncompliant use or mention, noncompliant sales, general use, or general mention. The study compares 2 methods for training machine learning models for text classification: single-sub-corpus transfer learning, in which a model is trained on tweets about a single drug and then tested on tweets about other drugs, and multi-sub-corpus incremental learning, in which models are trained on tweets about drugs in order of their structural similarity. The performance of a machine learning model trained on a single subcorpus (a data set of tweets about a specific category of drugs) was compared to the performance of a model trained on multiple subcorpora (data sets of tweets about multiple categories of drugs). Results: The results showed that the performance of the model trained on a single subcorpus varied depending on the specific drug used for training. The Tanimoto similarity (a measure of the structural similarity between compounds) was weakly correlated with the classification results. The model trained by transfer learning a corpus of drugs with close structural similarity performed better than the model trained by randomly adding a subcorpus when the number of subcorpora was small. Conclusions: The results suggest that structural similarity improves the classification performance of messages about unknown drugs if the drugs in the training corpus are few. On the other hand, this indicates that there is little need to consider the influence of the Tanimoto structural similarity if a sufficient variety of drugs are ensured. |
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| 書誌情報 |
en : Journal of Medical Internet Research 巻 25, 発行日 2023-05-03 |
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| 出版者 | ||||||||||
| 出版者 | JMIR Publications | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 1438-8871 | |||||||||
| 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/10.2196/44870 | |||||||||
| 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.jmir.org/2023/1/e44870/ | |||||||||
| 権利 | ||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||
| 権利情報 | $00A9Tomohiro Nishiyama, Shuntaro Yada, Shoko Wakamiya, Satoko Hori, Eiji Aramaki. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.05.2023. 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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | NA | |||||||||
