Why attend this webinar? Understand how text analytics can help you speed site selection, improve efficiency and support key decision making
It’s well-known that clinical trials are one of the most expensive parts of the drug development process; thus one of the goals of clinical trial professionals is to increase efficiency along the process. Using the ClinicalTrials.gov data set as an example, this webinar will demonstrate how I2E's powerful and agile text mining capabilities can be used to extract relevant information about clinical trials to support decision making.
ClinicalTrials.gov is a respected source of information about federally and privately supported clinical trials. This webinar looks at how to go beyond the conventional search approach to extract more precise facts and relationships.
During the clinical trials phase of drug development some of the key aims are to:
- Create a shortlist of existing clinical trial sites from which to select the most appropriate site for a new trial
- Monitor the status or progress of known competitors
Duration: 30 minutes including Q&A
Who should attend? Managers, specialists, informaticians involved in Clinical Trial planning & analytics, strategic product & pipeline intelligence, comparative effectiveness, real-world data access
About the webinar presenters
Dr. Jane Reed is the head of life science strategy 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 sciences informatics. She has 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.
Dr. Jim Dixon joined Linguamatics as an application specialist in January 2011. He first took advantage of text mining and NLP while working as a bioinformatician at Johnson & Johnson. His focus during this time was on target validation, biomarker discovery and disease pathways for areas such as cardiovascular, central nervous system and ocular diseases. Jim has a range of experience working with large amounts of information, from microarray data analysis during his postdoc, to reaction optimization software development for robotic chemistry workstations for his doctoral degree at North Carolina State University. He enjoys interacting with customers to understand their needs and applies text mining and NLP methods to transform their information into knowledge.