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Automatic mining of adverse drug reactions from social media posts and unstructured chats

It is estimated that adverse drug reactions (ADR) cost around $30 billion per year in the US only. Yet, most ADRs remain hidden – only around 5% of ADRs are reported to the regulator.

Marketing authorization holders, i.e., pharma companies, are required to monitor for suspected ADR in all own communication channels. It also includes web pages under their ownership, discussion of patient groups and special diseases groups, and mobile apps chats.

Many research studies are concerned with the use of general social media for adverse events mining – such as Twitter and Reddit.

We present a technology for ADR mining in social media posts and unstructured texts. The document is first classified for the presence of ADR. The adverse event is then extracted and related to the corresponding drug.

The presented system is based on Spark NLP/Spark NLP for Healthcare, the most widely used NLP library in the industry.

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