[HTML][HTML] Capturing the patient's perspective: a review of advances in natural language processing of health-related text
G Gonzalez-Hernandez, A Sarker… - Yearbook of medical …, 2017 - thieme-connect.com
Background: Natural Language Processing (NLP) methods are increasingly being utilized to
mine knowledge from unstructured health-related texts. Recent advances in noisy text …
mine knowledge from unstructured health-related texts. Recent advances in noisy text …
Adverse drug reaction classification with deep neural networks
We study the problem of detecting sentences describing adverse drug reactions (ADRs) and
frame the problem as binary classification. We investigate different neural network (NN) …
frame the problem as binary classification. We investigate different neural network (NN) …
Adverse drug event detection in tweets with semi-supervised convolutional neural networks
Current Adverse Drug Events (ADE) surveillance systems are often associated with a
sizable time lag before such events are published. Online social media such as Twitter could …
sizable time lag before such events are published. Online social media such as Twitter could …
[HTML][HTML] Advancing the state of the art in clinical natural language processing through shared tasks
M Filannino, Ö Uzuner - Yearbook of medical informatics, 2018 - thieme-connect.com
Objectives: To review the latest scientific challenges organized in clinical Natural Language
Processing (NLP) by highlighting the tasks, the most effective methodologies used, the data …
Processing (NLP) by highlighting the tasks, the most effective methodologies used, the data …
[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 …
Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions Using BERT Fine‐Tuned with FARM
Adverse drug reactions (ADRs) are the undesirable effects associated with the use of a drug
due to some pharmacological action of the drug. During the last few years, social media has …
due to some pharmacological action of the drug. During the last few years, social media has …
[HTML][HTML] Exploiting adversarial transfer learning for adverse drug reaction detection from texts
Abstract Adverse Drug Reactions (ADRs) are extremely hazardous to patients. ADR
Detection aims to automatically determine whether a sentence is related to an ADR, which is …
Detection aims to automatically determine whether a sentence is related to an ADR, which is …
Social media mining shared task workshop
Social media has evolved into a crucial resource for obtaining large volumes of real-time
information. The promise of social media has been realized by the public health domain …
information. The promise of social media has been realized by the public health domain …
Automated detection of adverse drug reactions from social media posts with machine learning
I Alimova, E Tutubalina - Analysis of Images, Social Networks and Texts …, 2018 - Springer
Adverse drug reactions can have serious consequences for patients. Social media is a
source of information useful for detecting previously unknown side effects from a drug since …
source of information useful for detecting previously unknown side effects from a drug since …
[HTML][HTML] Adversarial neural network with sentiment-aware attention for detecting adverse drug reactions
Adverse drug reaction (ADR) detection is an important issue in drug safety. ADRs are health
threats caused by medication. Identifying ADRs in a timely manner can reduce harm to …
threats caused by medication. Identifying ADRs in a timely manner can reduce harm to …