Filtering big data from social media - Building an early warning system for adverse drug reactions

Yang Ming; Kiang Melody; Shang Wei

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Filtering big data from social media - Building an early warning system for adverse drug reactions

机译：过滤来自社交媒体的大数据 - 构建一个用于不良药物的预警系统

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Objectives: Adverse drug reactions (ADRs) are believed to be a leading cause of death in the world. Pharmacovigilance systems are aimed at early detection of ADRs. With the popularity of social media, Web forums and discussion boards become important sources of data for consumers to shake their drug use experience, as a result may provide useful information on drugs and their adverse reactions. In this study, we propose an automated ADR related posts filtering mechanism using text classification methods. In real-life settings, ADR related messages are highly distributed in social media, while non-ADR related messages are unspecific and topically diverse. It is expensive to manually label a large amount of ADR related messages (positive examples) and non-ADR related messages (negative examples) to train classification systems. To mitigate this challenge, we examine the use of a partially supervised learning classification method to automate the process.

机译：目的：不利的药物反应（ADRS）被认为是世界上死亡的主要原因。药物检测系统旨在早期检测ADRS。随着社交媒体的普及，网络论坛和讨论板成为消费者撼动其吸毒经验的重要数据来源，因此可能提供有关药物的有用信息及其不良反应。在本研究中，我们提出了一种使用文本分类方法的自动化ADR相关帖子过滤机制。在现实生活中，ADR相关消息在社交媒体中高度分布，而非ADR相关的消息是无特异性和局部多样化的。手动标记大量ADR相关消息（正示例）和非ADR相关消息（否定示例）是昂贵的，以培训分类系统。为了缓解这一挑战，我们检查使用部分监督的学习分类方法来自动化过程。

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《Journal of biomedical informatics.》 |2015年第null期|共11页
作者
Yang Ming; Kiang Melody; Shang Wei;
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作者单位

Cent Univ Finance &

Econ Sch Informat Dept Informat Management Beijing 100081 Peoples R China;

Calif State Univ Long Beach Dept Informat Syst Long Beach CA 90840 USA;

Acad Math &

Syst Sci Beijing 100190 Peoples R China;

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原文格式 PDF
正文语种 eng
中图分类生物医学工程;
关键词
Partially supervised classification; Latent Dirichlet Allocation (LDA); Adverse drug reactions; Social media filtering; Social media mining;

机译：部分监督分类;潜在的Dirichlet分配（LDA）;不良药物反应;社交媒体过滤;社交媒体挖掘;

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专利

1. Filtering big data from social media - Building an early warning system for adverse drug reactions [J] . Yang Ming, Kiang Melody, Shang Wei Journal of biomedical informatics. . 2015,第Null期

机译：从社交媒体过滤大数据-建立药物不良反应预警系统
2. Adverse reaction profiles of hemorrhagic adverse reactions caused by direct oral anticoagulants analyzed using the Food and Drug Administration Adverse Event Reporting System (FAERS) database and the Japanese Adverse Drug Event Report (JADER) database [J] . Kazuyo Shimada, Shiori Hasegawa, Satoshi Nakao, International Journal of Medical Sciences . 2019,第9期

机译：使用食物和药物管理局的直接口服抗凝血剂引起的出血不良反应的不良反应谱不同，使用食品和药物管理局不良事件报告系统（FAEERS）数据库和日本不利药物事件报告（JADER）数据库
3. Filtering Entities to Optimize Identification of Adverse Drug Reaction From Social Media: How Can the Number of Words Between Entities in the Messages Help? [J] . Redhouane Abdellaoui, Nathalie Texier, Anita Burgun JMIR public health and surveillance. . 2017,第2期

机译：筛选实体以优化从社交媒体进行的药物不良反应的识别：消息中实体之间的单词数量如何提供帮助？
4. Extracting drug indications and adverse drug reactions from Spanish health social media [C] . Isabel Segura-Bedmar, Santiago de la Pena, Paloma Martinez Workshop on biomedical natural language processing . 2014

机译：从西班牙健康社交媒体中提取药物适应症和药物不良反应
5. Prediction of Drug Interaction and Adverse Reactions, with Data from Electronic Health Records, Clinical Reporting, Scientific Literature, and Social Media, Using Complexity Science Methods [D] . Brattig Correia, Rion. 2019

机译：使用复杂性科学方法通过电子健康记录，临床报告，科学文献和社交媒体中的数据预测药物相互作用和不良反应
6. Adverse reaction profiles of hemorrhagic adverse reactions caused by direct oral anticoagulants analyzed using the Food and Drug Administration Adverse Event Reporting System (FAERS) database and the Japanese Adverse Drug Event Report (JADER) database [O] . Kazuyo Shimada, Shiori Hasegawa, Satoshi Nakao, 2019

机译：使用食品药品监督管理局不良事件报告系统（FAERS）数据库和日本不良药品事件报告（JADER）数据库分析直接口服抗凝剂引起的出血性不良反应的不良反应概况
7. Filtering big data from social media – Building an early warning system for adverse drug reactions [O] . Yang Ming, Kiang Melody, Shang Wei 2015

机译：从社交媒体过滤大数据–建立药品不良反应预警系统
8. Drug Abuse Warning Network (DAWN) Report: Emergency Department Visits Involving Adverse Reactions to Medications among Children Aged 12 or Younger [R] . 2010

机译：药物滥用警告网络（DaWN）报告：急诊部门访问涉及12岁或12岁儿童的药物不良反应

Filtering big data from social media - Building an early warning system for adverse drug reactions

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