| Deep Learning for Adverse Event Detection From Web Search | |
Ahmad, Faizan1; Abbasi, Ahmed2; Kitchens, Brent3; Adjeroh, Donald4; Zeng, Daniel5
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| Source Publication | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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| ISSN | 1041-4347 |
| 2022-06-01 | |
| Volume | 34Issue:6Pages:2681-2695 |
| Corresponding Author | Abbasi, Ahmed(aabbasi@nd.edu) |
| Abstract | 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. |
| Keyword | 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 |
| WOS Keyword | DRUG-REACTIONS ; BAYESIAN NETWORKS ; IDENTIFICATION ; CLASSIFICATION |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | U.S. NSF ; MOST ; NNSFC Innovative Team ; CAS |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000789003800011 |
| Publisher | IEEE COMPUTER SOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/48414 |
| Collection | 复杂系统管理与控制国家重点实验室_互联网大数据与信息安全 |
| Corresponding Author | Abbasi, Ahmed |
| Affiliation | 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 |
| 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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