Named Entity Recognition (NER) autodetection and Adverse Events (AEs) prediction from Social Media and scientific journals using a Deep Learning approach

Social medias data bases are important for continuous and automated Adverse Drug Reactions (ADRs) surveillance. Predicting ADRs can reduce the related mortality. A systematic review of the medical scientific literature is required for tracking and identifying the risk/benefit ratio of drugs and safety issues.
The implementation of means of standardizing patient’s language used in social media such as Twitter will improve the precision of ADR detection and continuous surveillance at post-marketing phase of drugs (off-label drug uses included).

Generation of correlation hypotheses between Adverse Events (AEs) and NamedEntity Recognition (NER) of drugs in social media and scientific journals usinga machine learning approach

Pharmacovigilance (PV) has evolved and grown more complex over the past 5 to 10 years due to increasing data volumes, evolving regulations, influence of emerging markets and the emerging social media and innovative technological advances.
Fast detection of Adverse Drug Reactions (ADRs) could allow the pharmaceutical industry to anticipate and then to control more efficiently eventual risks associated to taking some medications.