Background The underreporting of adverse drug reactions (ADRs) through traditional reporting
channels is a limitation in the efficiency of the current pharmacovigilance system. Patients…

… drug and its adverse drug reaction (ADR) information are only available in narrative formats,
such as drug … Our results show that it is feasible to extract adverse drug reactions from drug …

… In this paper we explore a novel probabilistic model for drug categorization using a two-step …
of an adverse drug reaction, and then infer whether the combined comments for the drug (its …

… We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social
media, particularly from Twitter. Medical information extraction from social media is challenging…

… , their possible adverse reactions. We developed a system to automatically extract
mentions of Adverse Drug Reactions (ADRs) from user reviews about drugs in social network …

… We focus on the detection of Adverse Drug Reactions (ADRs): when a drug prescribed to
combat a disease can be the cause of other new diseases. In Fig. 1a some adverse …

… topic is to extract the ADR mentions … of drug/device reaction monitoring. An unsupervised
machine learning-based topic modeling approach with LDA algorithm is implemented to extract …

Adverse drug reactions (ADRs) are a main cause of morbidity and mortality in patients. Extracting
mentions of ADRs from the health-related text has important applications in biomedical …

… automatically extract ADRs from standardized European product labels, namely SmPC. To
answer the question, we first develop an NLP pipeline to extract adverse drug reactions from …

… extraction of Adverse Drug Reactions (ADRs) from different medical forum, social media like
Facebook, Twitter etc. studied yet. Drug reaction … views regarding drugs and its reactions in …