84 Hits in 4.4 sec

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  
In this work, we build several semi-supervised convolutional neural network (CNN) models for ADE classification in tweets, specifically leveraging different types of unlabeled data in developing the models  ...  Current Adverse Drug Events (ADE) surveillance systems are often associated with a sizable time lag before such events are published.  ...  ADE TWEET CLASSIFICATION WITH SEMI-SUPERVISED CNN To classify tweets that indicate adverse drug events, we use a semi-supervised Convolutionational Neural Networkbased (CNN) architecture (shown in Figure  ... 
doi:10.1145/3038912.3052671 dblp:conf/www/LeeQHDPLF17 fatcat:36xcplyzyvf3ddmtexcimngioa

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

Zhengguang Li, Hongfei Lin, Wei Zheng
2020 IEEE Access  
Finally, a convolutional neural network (CNN) model on the basis of bidirectional encoder representations from transformers (BERT) performed the classification task.  ...  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.  ...  CRNN is a convolutional neural network concatenated with a recurrent neural network with GRU as the basic RNN unit and RLU for the convolutional layer.  ... 
doi:10.1109/access.2020.2993169 fatcat:6y7q5ujfdbdh7ief7lfwevtgra

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.  ...  We formulate the problem as a binary classification task and compare the performance of four state-of-the-art attention-based neural networks in terms of the F-measure.  ...  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 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 results obtained are highly promising, showing the overall usefulness of neural network algorithms in this kind of tasks.  ...  Convolutional Neural Networks CNN is a hierarchical feed-forward neural network which structure is inspired by the biological visual system.  ... 
doi:10.1109/dexa.2017.34 dblp:conf/dexaw/AkhtyamovaAC17 fatcat:3wvozptwvvatbduggmllghfaiu

Deep Health Care Text Classification [article]

Vinayakumar R, Barathi Ganesh HB, Anand Kumar M, Soman KP
2017 arXiv   pre-print
This working note presents the Recurrent neural network (RNN) and Long short-term memory (LSTM) based embedding for automatic health text classification in the social media mining.  ...  Mostly, the existing methods are based on machine learning with knowledge-based learning.  ...  To make use of unlabeled data, 9 proposed semi-supervised approach based on Convolutional neural network for adverse drug event detection.  ... 
arXiv:1710.08396v1 fatcat:awqwjxwfzvh6dfgg4f2ci6v5x4

Multi-class Sentiment Analysis on Twitter

Venkatesh, Y. Nagaraju, Sheema Sultana, A. Mamthaj, R. Priyadarshini, S. Kavya
2020 Zenodo  
It demonstrates how far it is possible to go with the classification, and the limitations of handling the slang words, hashtags used in the tweets.  ...  To analyse opinion of user, it is necessary to go deeper in the classification and detect the sentiment hidden behind user post.  ...  Monitoring drug abuse, detecting the adverse effect of drugs by scanning the tweets with the drug names and finding its corresponding symptoms.  ... 
doi:10.5281/zenodo.4130218 fatcat:johmiw3aknbnxpmbd342obskji

Adverse drug reaction detection via a multihop self-attention mechanism

Tongxuan Zhang, Hongfei Lin, Yuqi Ren, Liang Yang, Bo Xu, Zhihao Yang, Jian Wang, Yijia Zhang
2019 BMC Bioinformatics  
The adverse reactions that are caused by drugs are potentially life-threatening problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their detrimental impacts on patients.  ...  With the growing amount of unstructured textual data, such as biomedical literature and electronic records, detecting ADRs in the available unstructured data has important implications for ADR research  ...  Abbreviations ADEs: Adverse drug events; ADRs: Adverse drug reactions; Bi-LSTM: Bidirectional long short-term memory; CNN: Convolutional neural network; DMNs: Dynamic memory networks; FAERS: The federal  ... 
doi:10.1186/s12859-019-3053-5 fatcat:amimvc6x7jhbth3dn6h76wqaji

A Sentiment Analysis Approach for Drug Reviews in Spanish

Karina Castro Pérez, José Luis Sánchez-Cervantes, María del Pilar Salas-Zárate, Luis Ángel Reyes Hernández, Lisbeth Rodríguez-Mazahua
2020 Research on computing science  
The rise in the application of opinion mining in recent years is a direct consequence of the growth of social networks and blogs that generate a large volume of unstructured data, however, the manual review  ...  Web Scraping and Natural Language Processing (NLP) techniques to know the users' experiences about drugs for chronic-degenerative diseases available in blogs, video blogs and specialized websites in Spanish  ...  Initiative Approach Polarity detection Data Source NLP Language Lee et al. [3] Semi-supervised neural network. convolutional ✔ Twitter X English Supervised Y.  ... 
dblp:journals/rcs/PerezSSHR20 fatcat:ck3xglb7qff2jpoblddh6uwn3e

Medical Concept Normalization for Online User-Generated Texts

Kathy Lee, Sadid A Hasan, Oladimeji Farri, Alok Choudhary, Ankit Agrawal
2017 2017 IEEE International Conference on Healthcare Informatics (ICHI)  
We use multiple deep learning architectures such as convolutional neural networks (CNN) and recurrent neural networks (RNN) with input word embeddings trained on various clinical domain-specific knowledge  ...  defined in standard clinical terminologies.  ...  ACKNOWLEDGMENT This work is supported in part by the following grants: NSF award CCF-1409601; DOE awards DE-SC0007456, DE-SC0014330, and Northwestern Data Science Initiative.  ... 
doi:10.1109/ichi.2017.59 dblp:conf/ichi/LeeHFCA17 fatcat:kzpngm2g5nefjmp63cqw3mcp5i

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.  ...  In this paper, we propose a method for the early detection of safety signals detection based on the frequency of adverse events reported by patients on social networks.  ... 
arXiv:2203.03538v1 fatcat:guji5cqfy5b6jb2m4k4tv3ixtu

Towards the Application of Machine Learning in Emergency Informatics [chapter]

Sharareh Rostam Niakan Kalhori
2022 Studies in Health Technology and Informatics  
Decision-support tools can apply the results of either supervised or various semi-supervised or unsupervised learning methods to tackle the how decisions about emergency situations are typically handled  ...  stable on the way to the care facility, and also avoiding adverse drug reactions, are some of the possible directions for exploring how ML can help to gather the data and to make emergency management  ...  This model to detect alarming posts of social media such as Twitter has been improved using deep learning methods such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) [22] [  ... 
doi:10.3233/shti220003 pmid:35593754 fatcat:5z752tot2ba5znp7wxyiaty67e

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.  ...  Lee et al. (2017) developed semi-supervised deep learning model on the Twitter corpus. In particular, they used the Convolution Neural Network (CNN) for classification.  ... 
doi:10.18653/v1/p19-1516 dblp:conf/acl/YadavESB19 fatcat:hteuwz2sh5hkzdvanj7bjcy5u4

A scoping review of the use of Twitter for public health research

Oduwa Edo-Osagie, Beatriz De La Iglesia, Iain Lake, Obaghe Edeghere
2020 Computers in Biology and Medicine  
From the reviewed literature, six domains for the application of Twitter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmacovigilance; (iv) Forecasting; (v) Disease  ...  disease, which algorithms and techniques were popular with each domain, and more.  ...  Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), word and document embeddings], statistical modelling and analysis (n = 12) [e.g. correclation analysis, partial differntial equation  ... 
doi:10.1016/j.compbiomed.2020.103770 pmid:32425212 pmcid:PMC7229729 fatcat:sxddp57merbbrm5ulftfu2ufsa

An overview of event extraction and its applications [article]

Jiangwei Liu, Liangyu Min, Xiaohong Huang
2021 arXiv   pre-print
With the rapid development of information technology, online platforms have produced enormous text resources.  ...  A trait of this survey is that it provides an overview in moderate complexity, avoiding involving too many details of particular approaches.  ...  For example, Peng et al. [14] propose an automatic pipeline to extract adverse drug events (ADE) by using Naïve Bayes and Support Vector Machine (SVM) to detect drugrelated tweets and sentiment analysis  ... 
arXiv:2111.03212v1 fatcat:o3oagnjrybh3vapvvp7twgjtuu

Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance

Oduwa Edo-Osagie, Gillian Smith, Iain Lake, Obaghe Edeghere, Beatriz De La Iglesia, Olalekan Uthman
2019 PLoS ONE  
We obtain good performance in classifying symptomatic tweets with both supervised and semi-supervised algorithms and found that the proposed semi-supervised algorithms preserve more of the relevant tweets  ...  In this paper, we propose a semi-supervised approach to symptomatic tweet classification and relevance filtering. We also propose alternative techniques to popular deep learning approaches.  ...  Acknowledgments We acknowledge support from NHS 111 and NHS Digital for their assistance with the NHS 111 system; Out-of-Hours providers submitting data to the GPOOH syndromic surveillance and Advanced  ... 
doi:10.1371/journal.pone.0210689 pmid:31318885 pmcid:PMC6638773 fatcat:2kqtjnjrjvcdxcfqaur6765imu
« Previous Showing results 1 — 15 out of 84 results