When:
Time: 8:00am PDT, 11:00am EDT, 4:00pm BST, 5:00pm CEST
Duration: 60 minutes
It’s well-known that clinical trials are one of the most expensive parts of the drug development process. Addressable bottlenecks include improving access to knowledge for protocol design such as appropriate biomarkers, clinical outcomes, eligibility criteria, assessment schedules, and more.
In addition, information from clinical literature is also valuable elsewhere in pharma organizations, feeding into competitive intelligence or key opinion leader strategies, for example.
Such extraction, usually done manually, is often tedious and error prone. IQVIA NLP (formerly Linguamatics) is a valuable technology for extracting and synthesizing the high value information that is found in unstructured text.
In this webinar, we will discuss the benefits of text analytics and natural language processing over internal and external clinical trial data to optimize clinical trial design and gain knowledge from legacy data.
Who should attend?
This webinar demonstrates some uses of NLP across clinical trial reports and protocols and will be of interest to anyone who wants to understand more about the power of NLP to gain more value from unstructured text within clinical development. Managers, specialists, informaticians involved in Clinical Trial planning & analytics, strategic product & pipeline intelligence, comparative effectiveness, real-world data access.
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.
