Real-time tracking of COVID-19 and coronavirus research updates through text mining

TL;DR

This paper presents an AI-driven text mining system that automatically sorts and clusters COVID-19 research publications, aiding scientists in efficiently accessing relevant information during the pandemic.

Contribution

The study introduces a novel text mining workflow utilizing clinical trial data, preclinical studies, and topic modeling to improve research efficiency on COVID-19 and other diseases.

Findings

Effective information extraction and clustering demonstrated

Workflow applicable to multiple disease areas

Publicly available modules for real-time updates

Abstract

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.