[HTML][HTML] Detecting potential adverse drug reactions using a deep neural network model
CS Wang, PJ Lin, CL Cheng, SH Tai… - Journal of medical …, 2019 - jmir.org
Background Adverse drug reactions (ADRs) are common and are the underlying cause of
over a million serious injuries and deaths each year. The most familiar method to detect …
over a million serious injuries and deaths each year. The most familiar method to detect …
[HTML][HTML] Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions
C McMaster, J Chan, DFL Liew, E Su… - Journal of biomedical …, 2023 - Elsevier
The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety
and risk-benefit profile of medications. With an incidence that has not changed over the last …
and risk-benefit profile of medications. With an incidence that has not changed over the last …
[HTML][HTML] Adverse drug reaction detection on social media with deep linguistic features
Y Zhang, S Cui, H Gao - Journal of biomedical informatics, 2020 - Elsevier
Adverse reactions caused by drugs are one of the most important public health problems.
Social media has encouraged more patients to share their drug use experiences and has …
Social media has encouraged more patients to share their drug use experiences and has …
Machine learning on adverse drug reactions for pharmacovigilance
CY Lee, YPP Chen - Drug discovery today, 2019 - Elsevier
An adverse drug reaction (ADR) is an unintended response to a drug that is noxious and the
reaction has a causal relationship to the drug 1, 2, 3, 4, 5. It is disturbing that∼ 3.6% of all …
reaction has a causal relationship to the drug 1, 2, 3, 4, 5. It is disturbing that∼ 3.6% of all …
[HTML][HTML] Adverse drug event discovery using biomedical literature: a big data neural network adventure
Background: The study of adverse drug events (ADEs) is a tenured topic in medical
literature. In recent years, increasing numbers of scientific articles and health-related social …
literature. In recent years, increasing numbers of scientific articles and health-related social …
A Large-scale CNN ensemble for medication safety analysis
Abstract 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 …
drug surveillance, and data from health-related forums and medical communities can be of a …
[HTML][HTML] Adverse drug reaction detection in social media by deep learning methods
Objective Health-related studies have been recently at the heart attention of the media.
Social media, such as Twitter, has become a valuable online tool to describe the early …
Social media, such as Twitter, has become a valuable online tool to describe the early …
Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets
Objective The primary objective of this review is to investigate the effectiveness of machine
learning and deep learning methodologies in the context of extracting adverse drug events …
learning and deep learning methodologies in the context of extracting adverse drug events …
Data-driven approach to detect and predict adverse drug reactions
TB Ho, L Le, DT Thai, S Taewijit - Current pharmaceutical …, 2016 - ingentaconnect.com
Background: Many factors that directly or indirectly cause adverse drug reaction (ADRs)
varying from pharmacological, immunological and genetic factors to ethnic, age, gender …
varying from pharmacological, immunological and genetic factors to ethnic, age, gender …
Adverse drug reaction detection via a multihop self-attention mechanism
Background The adverse reactions that are caused by drugs are potentially life-threatening
problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their …
problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their …