Mining Adverse Drug Reaction Mentions in Twitter with Word Embeddings
نویسنده
چکیده
This paper describes our system used in the PSB 2016 Workshop on Social Mining Shared Task for adverse drug reaction (ADR) extraction in Twitter. Our system uses Conditional Random Fields to train a classifier for extracting ADR mentions. We leverage word representations from large amount of unlabeled tweets, both drug related and generic. Our experiment results show that cluster features derived from word representations significantly improve Twitter ADR performances.
منابع مشابه
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 language in social media is highly informal, and user-expressed medical concepts are often nontechn...
متن کاملSocial Media Mining Shared Task Workshop
Social media has evolved into a crucial resource for obtaining large volumes of real-time information. The promise of social media has been realized by the public health domain, and recent research has addressed some important challenges in that domain by utilizing social media data. Tasks such as monitoring flu trends, viral disease outbreaks, medication abuse, and adverse drug reactions are s...
متن کاملTeam UKNLP: Detecting ADRs, Classifying Medication Intake Messages, and Normalizing ADR Mentions on Twitter
This paper describes the systems we developed for all three tasks of the 2nd Social Media Mining for Health Applications Shared Task at AMIA 2017. The first task focuses on identifying the Twitter posts containing mentions of adverse drug reactions (ADR). The second task focuses on automatic classification of medication intake messages (among those containing drug names) on Twitter. The last ta...
متن کاملExploring Brand-Name Drug Mentions on Twitter for Pharmacovigilance
Twitter has been proposed by several studies as a means to track public health trends such as influenza and Ebola outbreaks by analyzing user messages in order to measure different population features and interests. In this work we analyze the number and features of mentions on Twitter of drug brand names in order to explore the potential usefulness of the automated detection of drug side effec...
متن کاملEvent extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings
To extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to the same event. Hence, they group tweets into clusters based on the cooccurrence patterns of named entities and topical keywords. However, there are two main limitations. First, they ...
متن کامل