CORD-19 Search: Using Machine Learning to Explore COVID-19 Scientific Literature

AWS Education Research Seminar Series

Wednesday, July 29, 2020 | 10:00 AM PT / 1:00 PM ET

Session Abstract:
Healthcare providers and researchers face an exponentially increasing volume of information about COVID-19, which makes it difficult to derive trends that can inform treatment. In response, Amazon Web Services (AWS) launched CORD-19 Search, a search website powered by machine learning, to help researchers quickly search for research papers and documents to answer questions like “When is the salivary viral load highest for COVID-19?” Built on the Allen Institute for AI’s CORD-19 open research dataset of more than 130,000 research papers and other materials, this machine learning solution uses Amazon Comprehend Medical to extract relevant medical information from unstructured text. It also uses Amazon Kendra to deliver robust natural-language query capabilities to accelerate the pace of discovery. 

Join this session to explore the challenges faced by COVID-19 researchers and the role machine learning is playing in helping derive insights from the tens of thousands of scientific and medical papers on COVID-19.

  • Lucy Lu Wang, PhD — Young Investigator at the Allen Institute for AI
  • Kyle Lo — Applied Research Scientist at the Allen Institute for AI 
  • Taha Kass-Hout, MD — Director, Machine Learning and Chief Medical Officer at Amazon Web Services

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