When:
Time: 8:00am PDT, 11:00am EDT, 4:00pm BST, 5:00pm CEST
Duration: 0 minutes
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.
Join our webinar to learn how NLP can be used to synthesize information from many sources, provide evidence for business decisions, identify those novel connections that drive better value from your safety processing and how using NLP, CSL Behring doubled the level of autocoding adverse events from 30% to 60%.
Speakers
Jane Reed
Director, Life ScienceJane Reed
Jane Reed is Director of Life Science at Linguamatics. She is responsible for developing the strategic vision for Linguamatics’ growing product portfolio and business development in the life science domain. Jane has extensive experience in life science informatics. She worked for more than 15 years in vendor companies supplying data products, data integration and analysis, and consultancy to pharma and biotech—with roles at Instem, BioWisdom, Incyte, and Hexagen. Before moving into the life science industry, Jane worked in academia with post-docs in genetics and genomics.
Peng Zhang
Senior Application ScientistPeng Zhang
Peng Zhang is a Senior Application Scientist who provides NLP solutions for extracting information from unstructured data such as scientific literatures, electronic medical records and real world evidences. Before joining Linguamatics, he had >10 years’ experience of text-mining and informatics in pharmaceutical and biotechnology R&D setting.
