Finding Drug Side-effect Reviews in Social Media using Artificial Intelligence and Natural Language Processing


Although the safety of drugs is tested during clinical trials, many adverse drug reactions (ADR) may only be revealed under certain circumstances, for instance: long-term use, when used in conjunction with other drugs or by people excluded from trials, such as children or pregnant women.

Nowadays consumers often report their experiences with ADR on social media instead of traditional channels, which makes drug safety surveillance systems less efficient.


Thanks to our solution, drug safety surveillance systems can be easily improved by incorporating the knowledge extracted from social media into them.

Our system enables fast scanning of various data resources, such as: Twitter, Facebook or forums posts.

Then, using state-of-the-art Natural Language Processing techniques it filters out irrelevant information. The remaining, relevant data is processed and certain entities, such as: drug name, ADR, pharmaceutical company name, person age and gender, are extracted and analyzed.

The last step is automatic report generation. The executive summary containing all found ADR and insights on how they occurred is created and could be used in further drug development.

AI in pharma steps


  • More efficient identification of novel adverse drug reactions
  • Reducing cost of drug safety surveillance systems
  • Improving drug safety by finding potential ADR faster
  • Identified interactions could be used in further study and drug development