Real-time tracking of COVID-19 and coronavirus research updates through text mining
This system provides a tool for scientists to more efficiently research coronavirus by automatically sorting through a large volume of daily publications, addressing the challenge of information overload in rapidly evolving research fields.
This paper developed a text mining system to track COVID-19 and coronavirus research updates, processing hundreds of daily publications. It extracts information on clinical trials, preclinical studies, and general topics, providing a resource for scientists.
The novel coronavirus (SARS-CoV-2) which causes COVID-19 is an ongoing pandemic. There are ongoing studies with up to hundreds of publications uploaded to databases daily. We are exploring the use-case of artificial intelligence and natural language processing in order to efficiently sort through these publications. We demonstrate that clinical trial information, preclinical studies, and a general topic model can be used as text mining data intelligence tools for scientists all over the world to use as a resource for their own research. To evaluate our method, several metrics are used to measure the information extraction and clustering results. In addition, we demonstrate that our workflow not only have a use-case for COVID-19, but for other disease areas as well. Overall, our system aims to allow scientists to more efficiently research coronavirus. Our automatically updating modules are available on our information portal at https://ghddi-ailab.github.io/Targeting2019-nCoV/ for public viewing.