应用不平衡分类技术进行药物不良反应后分类

Chen-Kai Wang, Hong-Jie Dai, Feng-Duo Wang, E. C. Su
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引用次数: 5

摘要

如今,社交媒体经常被用户用来创建与他们健康相关的公共信息。随着社交媒体使用量的增加,人们发现了一种趋势,即用户创建与药物不良反应(ADR)相关的帖子。挖掘社交媒体数据的这些信息可以用于药理学上市后的监测和监测。然而,自动ADR检测系统的开发仍然具有挑战性,因为从现实世界的社交媒体编译的语料库通常高度不平衡,导致开发具有可靠性能的分类器存在障碍。在这项工作中,我们实现了各种不平衡技术,并比较了它们在为检测ADR帖子而发布的两个大型不平衡数据集上的性能。与为两个数据集开发的最先进的方法相比,基于更少的特征,开发的具有实现不平衡分类技术的分类器获得了相当甚至更好的f分数。
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Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques
Nowadays, social media is often being used by users to create public messages related to their health. With the increasing number of social media usage, a trend has been observed of users creating posts related to adverse drug reactions (ADR). Mining social media data for these information can be used for pharmacological post-marketing surveillance and monitoring. However, the development of automatic ADR detection systems remains challenging because the corpora compiled from real world social media were usually highly imbalanced resulting in barriers to develop classifiers with reliable performance. In this work, we implemented a variety of imbalanced techniques and compared their performance on two large imbalanced data sets released for the purpose of detecting ADR posts. Comparing with state-of-the-art approaches developed for the two dataset, based on much less features, the developed classifiers with implemented imbalanced classification techniques achieved comparable or even better F-scores.
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