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

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

scholarly journals Normalization of Long-tail Adverse Drug Reactions in Social Media

2020 ◽  
Author(s):  
Emmanouil Manousogiannis ◽  
Sepideh Mesbah ◽  
Alessandro Bozzon ◽  
Robert-Jan Sips ◽  
Zoltan Szlanik ◽  
...  
Keyword(s):  
Social Media ◽  
Adverse Drug Reactions ◽  
Drug Reactions ◽  
Long Tail

The role of advanced technologies supplemented with traditional methods in pharmacovigilance sciences

2020 ◽  
Vol 14 ◽  
Author(s):  
M Vijaya Satwika Naidu ◽  
Dudala Sai Sushma ◽  
Varun Jaiswal ◽  
S. Asha ◽  
Tarun Pal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Adverse Drug Reactions ◽  
Mobile Apps ◽  
Health Sector ◽  
Early Developmental Stage ◽  
Drug Reactions ◽  
Traditional Methods ◽  
Advanced Technologies

Background: The immediate automatic systemic monitoring and reporting of adverse drug reaction, improving the efficacy is the utmost need of medical informatics community. The venturing of advanced digital technologies into the health sector has opened new avenues for rapid monitoring. In recent years, data shared through social media, mobile apps and on other social websites has increased manifolds requiring data mining techniques. Objective: The objective of this report is to highlight the role of advanced technologies together with traditional methods to proactively aid in early detection of adverse drug reactions concerned with drug safety and pharmacovigilance. Methods: A thorough search was conducted for papers and patents regarding pharmacivigilance. All articles with respect to relevant subject were explored and mined from public repositories such as Pubmed, Google Scholar, Springer, ScienceDirect (Elsevier), Web of Science, etc. Results: The European Union’s Innovative Medicines Initiative WEB-RADR project emphasized the development of mobile applications and social media data for reporting adverse effects. Only relevant data has to be captured through the data mining algorithms (DMAs) playing an important role in timely prediction of risk with high accuracy using two popular approaches the frequentist and Bayesian approach. The pharmacovigilance at premarketing stage is useful for the prediction of the adverse drug reactions in early developmental stage of a drug. Later postmarketing safety reports and clinical data reports are important to be monitored through electronic health records, prescription-event monitoring, spontaneous reporting databases, etc approaches. Conclusion: The advanced technologies supplemented with traditional technologies is the need of hour for evaluating product’s risk profile and reducing risk in population esp. with comorbid conditions and on concomitant medications.

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