26 research outputs found
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction
Social media is an useful platform to share health-related information due to
its vast reach. This makes it a good candidate for public-health monitoring
tasks, specifically for pharmacovigilance. We study the problem of extraction
of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from
twitter. Medical information extraction from social media is challenging,
mainly due to short and highly information nature of text, as compared to more
technical and formal medical reports.
Current methods in ADR mention extraction relies on supervised learning
methods, which suffers from labeled data scarcity problem. The State-of-the-art
method uses deep neural networks, specifically a class of Recurrent Neural
Network (RNN) which are Long-Short-Term-Memory networks (LSTMs)
\cite{hochreiter1997long}. Deep neural networks, due to their large number of
free parameters relies heavily on large annotated corpora for learning the end
task. But in real-world, it is hard to get large labeled data, mainly due to
heavy cost associated with manual annotation. Towards this end, we propose a
novel semi-supervised learning based RNN model, which can leverage unlabeled
data also present in abundance on social media. Through experiments we
demonstrate the effectiveness of our method, achieving state-of-the-art
performance in ADR mention extraction.Comment: Accepted at DTMBIO workshop, CIKM 2017. To appear in BMC
Bioinformatics. Pls cite that versio
Towards reproducible research of event detection techniques for Twitter
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Selection of pseudo-annotated data for adverse drug reaction classification across drug groups
Automatic monitoring of adverse drug events (ADEs) or reactions (ADRs) is
currently receiving significant attention from the biomedical community. In
recent years, user-generated data on social media has become a valuable
resource for this task. Neural models have achieved impressive performance on
automatic text classification for ADR detection. Yet, training and evaluation
of these methods are carried out on user-generated texts about a targeted drug.
In this paper, we assess the robustness of state-of-the-art neural
architectures across different drug groups. We investigate several strategies
to use pseudo-labeled data in addition to a manually annotated train set.
Out-of-dataset experiments diagnose the bottleneck of supervised models in
terms of breakdown performance, while additional pseudo-labeled data improves
overall results regardless of the text selection strategy.Comment: Accepted to AIST 202
Review of trends in health social media analysis
This paper surveys recent publications (2008-2017) on using social media data to study public health. The survey describes the main topics being discussed in forums and presents short information about methods and tools used for analysis health social media. We put especial attention on adverse drug reaction detection problem (ADR)
Adverse drug extraction in twitter data using convolutional neural network
The study of health-related topics on social media has become a useful tool for the early detection of the different adverse medical conditions. In particular, it concerns cases related to the treatment of mental diseases, as the effects of medications here often prove to be unpredictable. In our research, we use convolutional neural networks (CNN) with word2vec embedding to classify user comments on Twitter. The aim of the classification is to reveal adverse drug reactions of users. The results obtained are highly promising, showing the overall usefulness of neural network algorithms in this kind of tasks
UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS
The relationship between drug and its side effects has been outlined in two websites: Sider and WebMD. The aim of this study was to find the association between drug and its side effects. We compared the reports of typical users of a web site called: "Ask a patient" website with reported drug side effects in reference sites such as Sider and WebMD. In addition, the typical users' comments on highly-commented drugs (Neurotic drugs, Anti-Pregnancy drugs and Gastrointestinal drugs) were analyzed, using deep learning method. To this end, typical users' comments on drugs' side effects, during last decades, were collected from the website “Ask a patient”. Then, the data on drugs were classified based on deep learning model (HAN) and the drugs' side effect. And the main topics of side effects for each group of drugs were identified and reported, through Sider and WebMD websites. Our model demonstrates its ability to accurately describe and label side effects in a temporal text corpus by a deep learning classifier which is shown to be an effective method to precisely discover the association between drugs and their side effects. Moreover, this model has the capability to immediately locate information in reference sites to recognize the side effect of new drugs, applicable for drug companies. This study suggests that the sensitivity of internet users and the diverse scientific findings are for the benefit of dis¬tinct detection of adverse effects of drugs, and deep learning would facilitate it