Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation

Journal of the American Medical Informatics Association : JAMIA
Jiaheng XieDaniel Dajun Zeng

Abstract

Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys. Social media provides a large data repository of consumers' e-cigarette feedback and experiences, which are useful for e-cigarette safety surveillance. However, it is difficult to automatically interpret the informal and nontechnical consumer vocabulary about e-cigarettes in social media. This issue hinders the use of social media content for e-cigarette safety surveillance. Recent developments in deep neural network methods have shown promise for named entity extraction from noisy text. Motivated by these observations, we aimed to design a deep neural network approach to extract e-cigarette safety information in social media. Our deep neural language model utilizes word embedding as the representation of text input and recognizes named entity types with the state-of-the-art Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Network. Our Bi-LSTM model achieved the best performance compared to 3 baseline models, with a precision of 94.10%, a recall of ...Continue Reading

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Citations

Dec 6, 2017·Therapeutic Advances in Drug Safety·Jiaheng XieZachary A Marcum
Oct 1, 2019·International Journal of Medical Informatics·Zhongzhi XuPaul Siu Fai Yip
Aug 28, 2021·International Journal of Environmental Research and Public Health·Melinda PénzesRóbert Urbán
Aug 29, 2021·Tobacco Control·Rui FuMichael Oliver Chaiton

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