Globally, thousands of scientists are working tirelessly on COVID-19 mitigation efforts, including the search for new treatments and a vaccine. Analytics leader SAS brings a powerful resource to the fight with COVID-19 Scientific Literature Search and Text Analysis, a free visual text analysis environment that uses artificial intelligence (AI) and machine learning to quickly search tens of thousands of research articles on COVID-19 and deliver potentially lifesaving answers to these scientists.
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Leading research groups have gathered and released to the public more than 50,000 full-text scientific research articles on COVID-19 and other coronaviruses through the COVID-19 Open Research Dataset (CORD-19). The articles include studies on treatment effectiveness, vaccine development, mitigation efforts, genetic analysis, economic impact and more. With so much scientific literature available, it’s impractical – if not impossible – to analyze it all manually.
“Effectively mining unstructured text from scientific literature can take teams of people, many needing deep subject matter expertise, and substantial amounts of time to effectively categorize and determine relevancy,” said Mark R. Cullen, MD, Professor of Medicine at Stanford University and Chair of the COVID-19 Research Database Scientific Steering Committee.
Drawing on AI, natural language processing, linguistic rules and sophisticated modeling techniques, SAS’ COVID-19 Scientific Literature Search and Text Analysis environment enables quick and intelligent extraction of relevant text and numerical data from COVID-19. The free and publicly available environment aims to quickly and efficiently connect the global research community with the most relevant scientific literature through its self-guided, cloud-based system.
