Building AI-powered Drug Adverse Events Monitoring Service

This webinar has been recorded and is available on YouTube.

According to the latest pharmacovigilance guidelines of the European Medicinal Agency, marketing authorization holders (MAH) are expected to:

• monitor international scientific and medical literature in order to detect new safety signals or emerging safety issues reported for medicinal products through a systematic literature review of widely used reference databases (e.g., Medline) no less frequently than once a week.
• review and assess reports of suspected adverse reactions from the scientific and medical literature (including relevant published abstracts from meetings and draft manuscripts) in order to identify and record ICSRs originating from spontaneous reports or non-interventional post-authorization studies.

Systematic literature reviewing and screening for safety signals has always been a resource-consuming process. On top, when done manually, it cannot guarantee the full coverage and capture of all important AERs data elements in a consistent way.

At this webinar, we will show you how this complex process can be automated with the help of Semantic Technology.

The webinar will demonstrate:

* Semantic data normalization over large public repositories of semi-structured drug adverse reaction information (FDA AERS);

* Automatic processing of scientific literature data feed with advanced NLP pipelines capable to extract causal relations between drug administration and adverse events;

* Integration of the raw extracted data into a robust QA (curation) process;

* Building a semantic search on top of the integrated knowledge from all sources.

Who is this webinar for:

Pharmacovigilance and Patient Safety information specialists

Expected duration: 30-40 min

Resource After Webinar: Recording