In an ideal world, adverse event reports would fit neatly into MedDRA, the standardized medical terminology for all regulatory submissions, so they could be easily reported and assessed for patterns to inform safety decisions. However, adverse events are often reported in natural language, for example by nurses, physicians and patients, each with a unique way of expressing themselves. To add to the complexity, these reporters have more reporting routes available than ever before, creating a deluge of natural language safety events that must be fully captured and understood.

Today's NLP technology can effectively "read" adverse event reports in their natural language, detect the adverse event and appropriate context, and code the adverse event to MedDRA. This can reduce the manual effort needed in safety case processing, and increase coding consistency, whether from safety verbatims, social media, published literature, or other unstructured text.

In this webinar, we will discuss NLP for safety coding in pharmacovigilance and safety assessment, providing case studies from life science clients such as CSL Behring and the FDA

Speakers:

Jane Reed
Director, Life Sciences, IQVIA

Peng Zhang
Senior Application Scientist, IQVIA