Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.
Objective: Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the l...
| Published in: | Journal of the American Medical Informatics Association Vol. 22; no. 3; pp. 671 - 682 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Oxford University Press / USA
May2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109744665&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109744665 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: May2015 vid: 22 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 109744665 NLM25755127 2013041020 10.1093/jamia/ocu041 NLM25755127 PMC4457113 109744665 ppf: 671 ppct: 11 formats: tig: atl: Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. aug: au: Nikfarjam, Azadeh Sarker, Abeed O'Connor, Karen Ginn, Rachel Gonzalez, Graciela sug: ab: Objective: Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media.Methods: We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique.Results: ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance.Conclusion: It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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