Deep Learning for Adverse Event Detection From Web Search
Ahmad, Faizan1; Abbasi, Ahmed2; Kitchens, Brent3; Adjeroh, Donald4; Zeng, Daniel5
Corresponding AuthorAbbasi, Ahmed(
AbstractAdverse 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.
KeywordEvent detection Drugs Deep learning Twitter Data mining Context modeling Automotive engineering Adverse event detection search queries deep learning auto encoders query embeddings user modeling
Indexed BySCI
Funding ProjectU.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]
Funding OrganizationU.S. NSF ; MOST ; NNSFC Innovative Team ; CAS
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000789003800011
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Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorAbbasi, Ahmed
Affiliation1.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
Recommended Citation
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.
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