Deep Learning for Adverse Event Detection From Web Search

doi:10.1109/TKDE.2020.3017786

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Deep Learning for Adverse Event Detection From Web Search
	Ahmad, Faizan 1; Abbasi, Ahmed 2; Kitchens, Brent 3; Adjeroh, Donald 4; Zeng, Daniel5
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
	2022-06-01
卷号	34 期号:6 页码:2681-2695
通讯作者	Abbasi, Ahmed(aabbasi@nd.edu)
摘要	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.
关键词	Event detection Drugs Deep learning Twitter Data mining Context modeling Automotive engineering Adverse event detection search queries deep learning auto encoders query embeddings user modeling
DOI	10.1109/TKDE.2020.3017786
关键词[WOS]	DRUG-REACTIONS ; BAYESIAN NETWORKS ; IDENTIFICATION ; CLASSIFICATION
收录类别	SCI
语种	英语
资助项目	U.S. NSF[IIS-1553109] ; U.S. NSF[IIS-1816504] ; U.S. NSF[BDS-1636933] ; U.S. NSF[CCF-1629450] ; U.S. NSF[IIS1552860] ; U.S. NSF[IIS-1816005] ; MOST[2019AAA0103405] ; MOST[2016QY02D0305] ; NNSFC Innovative Team[71621002] ; CAS[ZDRW-XH-2017-3] ; CAS[XDC02060600]
项目资助者	U.S. NSF ; MOST ; NNSFC Innovative Team ; CAS
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000789003800011
出版者	IEEE COMPUTER SOC
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48414
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Abbasi, Ahmed
作者单位	1.Univ Virginia, Comp Sci, Charlottesville, VA 22904 USA 2.Univ Notre Dame, IT Analyt & Operat, Notre Dame, IN 46556 USA 3.Univ Virginia, Informat Technol, Charlottesville, VA 22904 USA 4.West Virginia Univ, Lane Dept Comp Sci & Elect Engn, Morgantown, WV 26506 USA 5.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Ahmad, Faizan,Abbasi, Ahmed,Kitchens, Brent,et al. Deep Learning for Adverse Event Detection From Web Search[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2022,34(6):2681-2695.
APA	Ahmad, Faizan,Abbasi, Ahmed,Kitchens, Brent,Adjeroh, Donald,&Zeng, Daniel.(2022).Deep Learning for Adverse Event Detection From Web Search.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,34(6),2681-2695.
MLA	Ahmad, Faizan,et al."Deep Learning for Adverse Event Detection From Web Search".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 34.6(2022):2681-2695.