Deep Learning for Adverse Event Detection From Web Search
文献类型：期刊论文
| 作者 | Ahmad, Faizan2; Abbasi, Ahmed5; Kitchens, Brent1; Adjeroh, Donald4; Zeng, Daniel3
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| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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| 出版日期 | 2022-06-01 |
| 卷号 | 34期号:6页码:2681-2695 |
| 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 |
| ISSN号 | 1041-4347 |
| DOI | 10.1109/TKDE.2020.3017786 |
| 通讯作者 | Abbasi, Ahmed(aabbasi@nd.edu) |
| 英文摘要 | 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. |
| WOS关键词 | DRUG-REACTIONS ; BAYESIAN NETWORKS ; IDENTIFICATION ; CLASSIFICATION |
| 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] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000789003800011 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | U.S. NSF ; MOST ; NNSFC Innovative Team ; CAS |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48413]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 通讯作者 | Abbasi, Ahmed |
| 作者单位 | 1.Univ Virginia, Informat Technol, Charlottesville, VA 22904 USA 2.Univ Virginia, Comp Sci, Charlottesville, VA 22904 USA 3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 4.West Virginia Univ, Lane Dept Comp Sci & Elect Engn, Morgantown, WV 26506 USA 5.Univ Notre Dame, IT Analyt & Operat, Notre Dame, IN 46556 USA |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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