School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, South Australia 5095, Australia.
School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, South Australia 5095, Australia.
Int J Med Inform. 2018 Dec;120:157-171. doi: 10.1016/j.ijmedinf.2018.10.003. Epub 2018 Oct 13.
Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media.
Detecting ADEs whose drug and AE may be reported in different posts of a user leads to major concerns regarding the content authenticity and user credibility, which have not been addressed in previous studies. Content authenticity concerns whether a post mentions drugs or adverse events that are actually consumed or experienced by the writer. User credibility indicates the degree to which chronological evidence from a user's sequence of posts should be trusted in the ADE detection. We propose AC-SPASM, a Bayesian model for the authenticity and credibility aware detection of ADEs from social media. The model exploits the interaction between content authenticity, user credibility and ADE signal quality. In particular, we argue that the credibility of a user correlates with the user's consistency in reporting authentic content.
We conduct experiments on a real-world Twitter dataset containing 1.2 million posts from 13,178 users. Our benchmark set contains 22 drugs and 8089 AEs. AC-SPASM recognizes authentic posts with F - the harmonic mean of precision and recall of 80%, and estimates user credibility with precision@10 = 90% and NDCG@10 - a measure for top-10 ranking quality of 96%. Upon validation against known ADEs, AC-SPASM achieves F = 91%, outperforming state-of-the-art baseline models by 32% (p < 0.05). Also, AC-SPASM obtains precision@456 = 73% and NDCG@456 = 94% in detecting and prioritizing unknown potential ADE signals for further investigation. Furthermore, the results show that AC-SPASM is scalable to large datasets.
Our study demonstrates that taking into account the content authenticity and user credibility improves the detection of ADEs from social media. Our work generates hypotheses to reduce experts' guesswork in identifying unknown potential ADEs.
药物不良反应（ADE）是导致住院和死亡的主要原因之一。社交媒体是一种很有前途的开放数据源，可以及时发现潜在的 ADE。在本文中，我们研究了从社交媒体中检测 ADE 信号的问题。
检测药物和 AE 可能在用户的不同帖子中报告的 ADE 会引起对内容真实性和用户可信度的主要关注，而这在以前的研究中尚未得到解决。内容真实性问题是指帖子是否提到了作者实际服用或经历过的药物或不良事件。用户可信度表示应在 ADE 检测中信任用户帖子序列中的时间证据的程度。我们提出了 AC-SPASM，这是一种用于从社交媒体中检测 ADE 的真实性和可信度感知的贝叶斯模型。该模型利用了内容真实性、用户可信度和 ADE 信号质量之间的相互作用。特别是，我们认为用户的可信度与用户报告真实内容的一致性相关。
我们在包含 13178 个用户的 120 万条推文的真实世界 Twitter 数据集上进行了实验。我们的基准集包含 22 种药物和 8089 种 AE。AC-SPASM 识别真实帖子的 F 值为 80%，精确率和召回率的调和平均值，用户可信度的精度@10 为 90%，NDCG@10 - 前 10 名排名质量的度量为 96%。通过与已知的 ADE 进行验证，AC-SPASM 的 F 值为 91%，比最先进的基线模型高出 32%（p < 0.05）。此外，AC-SPASM 在检测和优先考虑进一步调查的未知潜在 ADE 信号方面，获得了精度@456 为 73%和 NDCG@456 为 94%。此外，结果表明 AC-SPASM 可扩展到大型数据集。
我们的研究表明，考虑到内容真实性和用户可信度可以提高社交媒体中 ADE 的检测能力。我们的工作提出了假设，可以减少专家在识别未知潜在 ADE 方面的猜测工作。
