Deep Learning for Adverse Event Detection From Web Search
Deep Learning for Adverse Event Detection From Web Search
复制标题
DOI：
10.1109/tkde.2020.3017786
复制
发表时间：
2022-06
影响因子：
8.9
通讯作者：
Faizan Ahmad;A. Abbasi;Brent Kitchens;D. Adjeroh;D. Zeng
中科院分区：
文献类型：
--
作者：
Faizan Ahmad;A. Abbasi;Brent Kitchens;D. Adjeroh;D. Zeng
不良事件检测对于许多实际应用至关重要，包括及时识别产品缺陷、灾难和重大社会政治事件。在健康方面，药物不良事件每年造成无数住院和死亡。由于用户经常开始他们的信息寻求和报告与在线搜索，搜索查询日志的检查已成为一个重要的检测渠道。然而，搜索上下文（包括查询意图和用户行为的异质性）对于从搜索查询中提取信息非常重要，但测量和分析这些方面的挑战已经排除了它们在先前研究中的使用。我们提出了DeepSAVE，这是一种新的深度学习框架，用于基于用户搜索查询日志检测不良事件。DeepSAVE使用了一个丰富的变分自动编码器，包括一个新颖的查询嵌入和用户建模模块，它们协同工作，以解决与基于搜索的不良事件检测相关的上下文挑战。三个大型真实事件数据集的评估结果表明，DeepSAVE优于现有的检测方法以及比较深度学习自动编码器。消融分析表明，DeepSAVE的每个组件对其整体性能都有显著贡献。总的来说，结果证明了所提出的架构的可行性，从搜索查询日志中检测不良事件。
Adverse event detection is critical for many real-world applications including timely identification of product defects, disasters, and major socio-political incidents. In the health context, adverse drug events account for countless hospitalizations and deaths annually. Since users often begin their information seeking and reporting with online searches, examination of search query logs has emerged as an important detection channel. However, search context - including query intent and heterogeneity in user behaviors – is extremely important for extracting information from search queries, and yet the challenge of measuring and analyzing these aspects has precluded their use in prior studies. We propose DeepSAVE, a novel deep learning framework for detecting adverse events based on user search query logs. DeepSAVE uses an enriched variational autoencoder encompassing a novel query embedding and user modeling module that work in concert to address the context challenge associated with search-based detection of adverse events. Evaluation results on three large real-world event datasets show that DeepSAVE outperforms existing detection methods as well as comparison deep learning auto encoders. Ablation analysis reveals that each component of DeepSAVE significantly contributes to its overall performance. Collectively, the results demonstrate the viability of the proposed architecture for detecting adverse events from search query logs.