Computational Biology Research Lab, Department of Computer Science National University of Computer and Emerging Sciences.
Email:
AMIA Annu Symp Proc. 2021 Jan 25;2020:442-451. eCollection 2020.
The development of novel drugs in response to changing clinical requirements is a complex and costly method with uncertain outcomes. Postmarket pharmacovigilance is essential as drugs often have under-reported side effects. This study intends to use the power of digital media to discover the under-reported side effects of marketed drugs. We have collected tweets for 11 different Drugs (Alprazolam, Adderall, Fluoxetine, Venlafaxine, Adalimumab, Lamotrigine, Quetiapine, Trazodone, Paroxetine, Metronidazole and Miconazole). We have compiled a vast adverse drug reactions (ADRs) lexicon that is used to filter health related data. We constructed machine learning models for automatically annotating the huge amount of publicly available Twitter data. Our results show that on average 43 known ADRs are shared between Twitter and FAERS datasets. Moreover, we were able to recover on average 7 known side effects from Twitter data that are not reported on FAERS. Our results on Twitter dataset show a high concordance with FAERS, Medeffect and Drugs.com. Moreover, we manually validated some of the under-reported side effect predicted by our model using literature search. Common known and under-reported side effects can be found at https://github.com/cbrl-nuces/Leveraging-digital-media-data-for-pharmacovigilance.
针对不断变化的临床需求开发新药物是一种复杂且昂贵的方法，其结果具有不确定性。药物上市后的药物警戒至关重要，因为药物常常会有报告不足的副作用。本研究旨在利用数字媒体的力量来发现已上市药物的报告不足的副作用。我们已经为 11 种不同的药物（阿普唑仑、安非他命、氟西汀、文拉法辛、阿达木单抗、拉莫三嗪、喹硫平、曲唑酮、帕罗西汀、甲硝唑和咪康唑）收集了推文。我们编写了一个庞大的药物不良反应（ADR）词典，用于筛选与健康相关的数据。我们构建了机器学习模型，以自动注释大量公开的 Twitter 数据。我们的结果表明，Twitter 和 FAERS 数据集平均有 43 个已知的 ADR 是共有的。此外，我们还能够从 Twitter 数据中恢复平均 7 种 FAERS 未报告的已知副作用。我们在 Twitter 数据集上的结果与 FAERS、Medeffect 和 Drugs.com 高度一致。此外，我们还使用文献搜索手动验证了一些我们的模型预测的报告不足的副作用。常见的已知和报告不足的副作用可以在 https://github.com/cbrl-nuces/Leveraging-digital-media-data-for-pharmacovigilance 上找到。
