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

2018 ◽  
Vol 2 (1-2) ◽  
pp. 25-43 ◽  
Author(s):  
Aaron J. Masino ◽  
Daniel Forsyth ◽  
Alexander G. Fiks
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Adverse Drug Reactions ◽  
Word Embedding ◽  
Drug Reactions

Prediction of Adverse Drug Reactions Using Drug Convolutional Neural Networks

2020 ◽  
Author(s):  
Anjani Sankar Mantripragada ◽  
Sai Phani Teja ◽  
Rohith Reddy Katasani ◽  
Pratik Joshi ◽  
V. Masilamani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Adverse Drug Reactions ◽  
Drug Reactions

Detecting adverse drug reactions from social media based on multi-channel convolutional neural networks

2018 ◽  
Vol 31 (9) ◽  
pp. 4799-4808 ◽  
Author(s):  
Chen Shen ◽  
Hongfei Lin ◽  
Kai Guo ◽  
Kan Xu ◽  
Zhihao Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Convolutional Neural Networks ◽  
Adverse Drug Reactions ◽  
Drug Reactions

scholarly journals Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13967-13968
Author(s):  
Yuxiang Xie ◽  
Hua Xu ◽  
Congcong Yang ◽  
Kai Gao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Word Embedding ◽  
Vector Representation ◽  
Adversarial Learning ◽  
Model Experiments ◽  
Robust Model ◽  
Adversarial Training ◽  
Relation Classification

The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. However, DS also brings the problem of wrong labeling. Contrary to DS, the few-shot method relies on few supervised data to predict the unseen classes. In this paper, we use word embedding and position embedding to construct multi-channel vector representation and use the multi-channel convolutional method to extract features of sentences. Moreover, in order to alleviate few-shot learning to be sensitive to overfitting, we introduce adversarial learning for training a robust model. Experiments on the FewRel dataset show that our model achieves significant and consistent improvements on few-shot RC as compared with baselines.

scholarly journals Text Classification Based on Convolutional Neural Networks and Word Embedding for Low-Resource Languages: Tigrinya

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 52
Author(s):  
Awet Fesseha ◽  
Shengwu Xiong ◽  
Eshete Derb Emiru ◽  
Moussa Diallo ◽  
Abdelghani Dahou
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
Word Embedding ◽  
Northern Ethiopia ◽  
Classification Problems ◽  
Semitic Languages ◽  
Low Resource ◽  
Free Data ◽  
Class Labels

This article studies convolutional neural networks for Tigrinya (also referred to as Tigrigna), which is a family of Semitic languages spoken in Eritrea and northern Ethiopia. Tigrinya is a “low-resource” language and is notable in terms of the absence of comprehensive and free data. Furthermore, it is characterized as one of the most semantically and syntactically complex languages in the world, similar to other Semitic languages. To the best of our knowledge, no previous research has been conducted on the state-of-the-art embedding technique that is shown here. We investigate which word representation methods perform better in terms of learning for single-label text classification problems, which are common when dealing with morphologically rich and complex languages. Manually annotated datasets are used here, where one contains 30,000 Tigrinya news texts from various sources with six categories of “sport”, “agriculture”, “politics”, “religion”, “education”, and “health” and one unannotated corpus that contains more than six million words. In this paper, we explore pretrained word embedding architectures using various convolutional neural networks (CNNs) to predict class labels. We construct a CNN with a continuous bag-of-words (CBOW) method, a CNN with a skip-gram method, and CNNs with and without word2vec and FastText to evaluate Tigrinya news articles. We also compare the CNN results with traditional machine learning models and evaluate the results in terms of the accuracy, precision, recall, and F1 scoring techniques. The CBOW CNN with word2vec achieves the best accuracy with 93.41%, significantly improving the accuracy for Tigrinya news classification.

DeepPatent: patent classification with convolutional neural networks and word embedding

2018 ◽  
Vol 117 (2) ◽  
pp. 721-744 ◽  
Author(s):  
Shaobo Li ◽  
Jie Hu ◽  
Yuxin Cui ◽  
Jianjun Hu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Word Embedding ◽  
Patent Classification

scholarly journals Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0236789
Author(s):  
Shungo Imai ◽  
Yoh Takekuma ◽  
Hitoshi Kashiwagi ◽  
Takayuki Miyai ◽  
Masaki Kobayashi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Practice ◽  
Risk Prediction ◽  
Adverse Drug Reactions ◽  
Drug Reactions ◽  
Artificial Neural

scholarly journals Visualising Patterns Associated with Adverse Drug Reactions in French Forums

2021 ◽  
Author(s):  
Nour Allam ◽  
Bissan Audeh ◽  
Marie-Christine Jaulent ◽  
Cedric Bousquet
Keyword(s):  
Social Media ◽  
Adverse Drug Reactions ◽  
Word Embedding ◽  
Discussion Forums ◽  
Drug Reactions ◽  
Source Of Information ◽  
Visualisation Method

As social media are an interesting source of information for pharmacovigilance, we implemented a novel visualisation method for pharmacovigilance specialists applied to French discussion forums. A word embedding model was trained on posts to facilitate the identification of patterns associated with adverse drug reactions.

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