- Why is text mining so important?
- What are the typical challenges and research questions in this area?
- Are there any typical ones for medical/pharmaceutical research?
- What data sources could we tap into?
- What are some foundational concepts in the natural language process?
In this episode, I speak with Jennings Xu, who works as a Director at Quid – an AI natural language processing company.
Beyond the questions above, we’ll address these topics:
- What is zettabyte
- What are the different modes of data
- Where in the medical field is the biggest problem among text data
- What concepts are used in text mining
- What is the difference between text mining and natural language processing
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Jennings Xu
Director at Quid
Jennings Xu leads their healthcare enterprise team in developing AI-powered tech solutions to help guide strategy for pharma and provider clients, including algorithmically reading scientific literature at scale, leveraging predictive analytics for biotech asset evaluation, and mapping key opinion leaders and emerging innovation landscapes. Before joining Quid, he was at McKinsey & Co. driving transformational pharma, provider, and healthcare supply chain projects, studied medicine at Case Western Reserve University, and led computational research for children with autism at Yale University after completing his BA in Biology from Harvard University. Jennings has been published in Nature, Oncogene, and recently co-authored an NEJM Catalyst article on using A.I. to hear half a million chronic patient comments
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