Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.
Objective: 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 provide...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 25; no. 1; pp. 72 - 81 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127021680&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127021680 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jan2018 vid: 25 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 127021680 127021680 NLM28505280 127021680 10.1093/jamia/ocx045 NLM28505280 127021680 ppf: 72 ppct: 9 formats: tig: atl: Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation. aug: au: Xie, Jiaheng Liu, Xiao Zeng, Daniel Dajun Dajun Zeng, Daniel affil: Department of Management Information Systems, University of Arizona, Tucson, AZ, USA sug: subj: Neural Networks (Computer) Data Mining Methods Social Media Semantics Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Barthel Index Short Portable Mental Status Questionnaire Social Readjustment Rating Scale ab: Objective: 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.Methods: 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.Results: Our Bi-LSTM model achieved the best performance compared to 3 baseline models, with a precision of 94.10%, a recall of 91.80%, and an F-measure of 92.94%. We identified 1591 unique adverse events and 9930 unique e-cigarette components (ie, chemicals, flavors, and devices) from our research testbed.Conclusion: Although the conditional random field baseline model had slightly better precision than our approach, our Bi-LSTM model achieved much higher recall, resulting in the best F-measure. Our method can be generalized to extract medical concepts from social media for other medical applications. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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