Artificial Intelligence Pharma – Drug Side-effect Detection

Note: This article was originally published on November 28, 2017 and has been migrated from our previous blog. Some details — tools, libraries, benchmarks, industry context — may be outdated. For our latest perspective, see our recent posts.

Challenge

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.

Solution

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 forum 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, and patient’s 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.

Benefits

• 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