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Pharmacovigilance is defined as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem. Recognizable proof of Adverse Drug Reactions (ADRs) amid the post-marketing stage is a standout amongst the most essential objectives of goals of drug safety surveillance. Food and Drug Administration (FDA) uses The Adverse Event Reporting System (AERS) to monitor new safety concerns related to a marketed product, ensuring compliance to reporting regulations and responding to outside requests for information. The FDA receives adverse event and medication error reports directly from multiple sources, healthcare professionals including physicians, pharmacists and others and from consumers including patients, lawyers and others. The data directly collected from physicians and patients suffer from a range of limitations including under-reporting (only approximately 10% of serious ADRs are reported in AERS), over-reporting of known ADRs and incomplete data. Thus, in recent times, research focus has broadened to the utilization of other sources of data for ADR detection. In recent times, Internet has opened opportunities for public to share their opinion and feeling over Social Media, thus making it as an indispensable source for Pharmacovigilance studies. This paper presents a combined approach for Pharmacovigilance using Big Data Technology, Natural Language Processing (NLP) and public domain knowledge. The Big Data Architecture is designed in a way to capture streaming data from Twitter and web-scraped data from Daily Strength website, to capture real time reporting of ADR and storing in Hadoop File System. A retrospective analysis of public social media data is conducted for numerous post market drugs using both lexicon-based models and Deep Learning models of LSTM family. Automated classifiers are being used to identify each post with resemblance to an adverse event among English language posts and compared with existing work. The work shows promising results for deploying at mass scale for real time identification of ADR and hence improving safe Drug use across the world.
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