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Detecting Twitter posts with Adverse Drug Reactions using Convolutional Neural Networks

Sarthak Jain, Xun Peng, Byron C. Wallace
2017 American Medical Informatics Association Annual Symposium  
We describe our system for Shared Task 1, which involves recognizing tweets containing adverse drug reaction mentions.  ...  We used a relatively standard CNN architecture coupled with task-specific features and pre-processing steps to achieve an F-score on the test set of 0.412, placing us as the third best-scoring team and  ...  Convolutional Neural Network Convolutional Neural Networks (CNN)s have been extensively used in many computer vision and, more recently, NLP tasks.  ... 
dblp:conf/amia/JainPW17 fatcat:x7ear7fpb5fz7p5psxivtxnof4

Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks

Ilseyar Alimova, Elena Tutubalina
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop  
Detection of adverse drug reactions in postapproval periods is a crucial challenge for pharmacology.  ...  In this work, we focus on extraction information of adverse drug reactions from various sources of biomedical textbased information, including biomedical literature and social media.  ...  Automated detection of adverse drug reactions from social media posts with machine learning. In International Conference on Analysis of Images, Social Networks and Texts, pages 3-15. Springer.  ... 
doi:10.18653/v1/p19-2058 dblp:conf/acl/AlimovaT19 fatcat:ohj3gsflqfbyfpath6cywdahv4

Adverse Drug Reaction Classification With Deep Neural Networks

Trung Huynh, Yulan He, Alistair Willis, Stefan Rueger
2016 International Conference on Computational Linguistics  
We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification.  ...  In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural  ...  Table 1 : 1 Adverse drug reaction classification results on the Twitter and ADE datasets.  ... 
dblp:conf/coling/HuynhHWR16 fatcat:l7z3lz3q5fhm3ca3xec6gv7ldq

Adverse Drug Extraction in Twitter Data Using Convolutional Neural Network

Liliya Akhtyamova, Mikhail Alexandrov, John Cardiff
2017 2017 28th International Workshop on Database and Expert Systems Applications (DEXA)  
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 study of health-related topics on social media has become a useful tool for the early detection of the different adverse medical conditions.  ...  INTRODUCTION According to the Agency for Healthcare Research and Quality over 770,000 people are injured or die each year in hospitals from adverse drug reactions [1] (ADRs), making the early detection  ... 
doi:10.1109/dexa.2017.34 dblp:conf/dexaw/AkhtyamovaAC17 fatcat:3wvozptwvvatbduggmllghfaiu

A Large-Scale CNN Ensemble for Medication Safety Analysis [article]

Liliya Akhtyamova, Andrey Ignatov, John Cardiff
2017 arXiv   pre-print
Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing drug surveillance, and data from health-related forums and medical communities can be of a great significance for estimating  ...  We present an architecture that is based on a vast ensemble of CNNs with varied structural parameters, where the prediction is determined by the majority vote.  ...  Introduction Monitoring Adverse Drug Reactions (ADR) -unintended responses to a drug when it is used at recommended dosage levels, has a direct relationship with the public health and healthcare costs  ... 
arXiv:1706.05549v1 fatcat:iduw42taxvahrpagt52muuiewm

Did you take the pill? - Detecting Personal Intake of Medicine from Twitter [article]

Debanjan Mahata, Jasper Friedrichs, Rajiv Ratn Shah, Jing Jiang
2018 arXiv   pre-print
., Twitter), have been used for monitoring drug abuse, adverse reactions of drug usage and analyzing expression of sentiments related to drugs.  ...  We train a stacked ensemble of shallow convolutional neural network (CNN) models on an annotated dataset.  ...  task using convolutional neural networks.  ... 
arXiv:1808.02082v1 fatcat:vxxpx4ir7bdgpeaglr2knpwy4e

An effective emotional expression and knowledge-enhanced method for detecting adverse drug reactions

Zhengguang Li, Hongfei Lin, Wei Zheng
2020 IEEE Access  
Moreover, most of the systems make less use of medical knowledge to enhance the detection of the potential relationship between drugs and adverse reactions in posts.  ...  Finally, a convolutional neural network (CNN) model on the basis of bidirectional encoder representations from transformers (BERT) performed the classification task.  ...  Then, posts with co-occurrence drug names and adverse reactions are the main objectives in the extraction of adverse reactions.  ... 
doi:10.1109/access.2020.2993169 fatcat:6y7q5ujfdbdh7ief7lfwevtgra

Adverse Drug Reaction Detection in Social Media by Deepm Learning Methods

Zahra Rezaei, Hossein Ebrahimpour-Komleh, Behnaz Eslami, Ramyar Chavoshinejad, Mehdi Totonchi
2019 Cell journal  
Social media, such as Twitter, has become a valuable online tool to describe the early detection of various adverse drug reactions (ADRs).  ...  We used deep learning methods with the word2vec to classify ADR comments posted by the users and present an architecture by HAN, FastText, and CNN.  ...  , ASKA; Ask a patient, CNN; Convolutional neural network, and HAN; Hierarchical attention network.  ... 
doi:10.22074/cellj.2020.6615 pmid:31863657 pmcid:PMC6947008 fatcat:kizwy4mi7nbflfs37qq56zmouu

Adverse Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks

Kathy Lee, Ashequl Qadir, Sadid A. Hasan, Vivek Datla, Aaditya Prakash, Joey Liu, Oladimeji Farri
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
Online social media such as Twitter could describe adverse drug events in real-time, prior to official reporting.  ...  Current Adverse Drug Events (ADE) surveillance systems are often associated with a sizable time lag before such events are published.  ...  Supervised CNN is a supervised convolutional neural network classifier trained only on labeled tweets.  ... 
doi:10.1145/3038912.3052671 dblp:conf/www/LeeQHDPLF17 fatcat:36xcplyzyvf3ddmtexcimngioa

Active Neural Networks to Detect Mentions of Changes to Medication Treatment in Social Media [article]

Davy Weissenbacher, Suyu Ge, Ari Klein, Karen O'Connor, Robert Gross, Sean Hennessy, Graciela Gonzalez-Hernandez
2020 medRxiv   pre-print
Methods: We annotated 9,835 tweets mentioning medications and trained a convolutional neural network (CNN) to find mentions of medication treatment changes, regardless of whether the change was recommended  ...  We used active and transfer learning from 12,972 reviews we annotated from WebMD to address the class imbalance of our Twitter corpus.  ...  Convolutional Neural Network We selected Convolutional Neural Networks (CNNs) to detect posts stating a medication change.  ... 
doi:10.1101/2020.12.04.20244210 fatcat:f7bowgbxr5ct7cumwrfbycya2u

Detecting Adverse Drug Reactions on Twitter with Convolutional Neural Networks and Word Embedding Features

Aaron J. Masino, Daniel Forsyth, Alexander G. Fiks
2018 Journal of Healthcare Informatics Research  
Motivated by limitations of adverse drug reaction (ADR) detection in clinical trials and passive post-market drug safety surveillance systems, a number of researchers have examined social media data as  ...  We developed a convolutional neural network model (ConvNet) that processes tweets as represented by word vectors created using unsupervised learning on large datasets.  ...  In particular, they are unable to differentiate adverse reactions from indications (the reason the drug was used).  ... 
doi:10.1007/s41666-018-0018-9 pmid:35415401 pmcid:PMC8982795 fatcat:clr3abofzfeabpufr6wmndztny

AI-based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox Scandal in 2017 on Doctissimo Forum [article]

Valentin Roche, Jean-Philippe Robert, Hanan Salam
2022 arXiv   pre-print
Various approaches have investigated the analysis of social media data using AI such as NLP techniques for detecting adverse drug events.  ...  sentiment analysis. (2) We propose a deep learning architecture, named Word Cloud Convolutional Neural Network (WC-CNN) which trains a CNN on word clouds extracted from the patients comments.  ...  Adverse Drug Reactions Analysis. We study the evolution of Adverse Drug Reactions occurrence in the patients comments. For this we develop a method for the detection of Adverse Drug Reactions.  ... 
arXiv:2203.03538v1 fatcat:guji5cqfy5b6jb2m4k4tv3ixtu

#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter [article]

Debanjan Mahata, Jasper Friedrichs, Hitkul, Rajiv Ratn Shah
2018 arXiv   pre-print
., Twitter), have been used for monitoring drug abuse, adverse reactions of drug usage and analyzing expression of sentiments related to drugs.  ...  We also design and train a new architecture of a stacked ensemble of shallow convolutional neural network (CNN) ensembles.  ...  adverse drug reactions , abuse (Hanson et al., 2013) , and user sentiment (Korkontzelos et al., 2016) .  ... 
arXiv:1805.06375v1 fatcat:jstwq5z4rbg77j6utpthj2kqsy

Drug Reaction Discriminator within Encoder-Decoder Neural Network Model: COVID-19 Pandemic Case Study

Hanane Grissette, El Habib Nfaoui
2020 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS)  
Most of them consisted of only detecting adverse drug reaction(ADR), but may fail to retrieve other aspect, e.g, the beneficial drug reaction or drug retroviral effects such as "relieve intraocular pressure  ...  Few approaches have proposed in this matter, especially for detecting different drug reaction descriptions from patients generated narratives on social networks.  ...  Thus, we resume related studies that deal, especially, with Drug reaction detection and normalization. 1) Drug reaction detection: As discussed in Section.I, these platforms are becoming an increasingly  ... 
doi:10.1109/snams52053.2020.9336561 fatcat:co7uqgtuunaixnd5tunorkqsd4

A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance.  ...  In this paper, we propose a neural network inspired multitask learning framework that can simultaneously extract ADRs from various sources.  ...  use very implicit and creative language to describe their adverse drug reaction.  ... 
doi:10.18653/v1/p19-1516 dblp:conf/acl/YadavESB19 fatcat:hteuwz2sh5hkzdvanj7bjcy5u4
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