COVID-19 Knowledge Distiller
In a pandemic situation, clinicians and researchers are in urgent need of rapid and quality information that will help them to inform diagnostics and therapeutics relating to the disease. We were tasked to provide them solutions for the same
Background: Traditional research models producing trustworthy and methodologically sound results takes time, which does not fit well with a pandemic context where research has to be fast-tracked. The ongoing coronavirus disease 2019 (COVID-19) pandemic has demonstrated the volume and velocity of scientific information that can be produced in a short period of time. For COVID-19, some of these traditional delays have been circumvented as many medical journals have prioritized publications related to COVID-19 and there is greater use of preprint servers to make research findings immediately available in an open format.
In the context of COVID-19 pandemic, researchers and clinicians require a reliable model to mine published literature for novel insights, emerging risk factors and therapeutics to inform their work in combating the COVID-19 pandemic
Problem Statement: We need to present an innovative text mining and analytical tool that will aid clinicians and researchers in extracting valuable insights from large datasets of literature.
For the purpose of the task, we formed a team including data scientists, software engineers, clinicians and medical researchers to enable a credible and informed approach in developing the text-mining model. Our text-mining model automates the knowledge discovery process aiding researchers and clinicians in their pursuit of appropriate treatment and management of COVID-19 cases. This process is achieved by identifying whether a given medical article is related to COVID-19, and it’s relevance to the competition task of identifying clinical risk factors embedded in the literature. The assumption here is that supplied databases collectively have relevant information suitable for extraction. While the tool we developed here was customised to automatically identify COVID19 related risk factors, this model can be potentially expanded to extract useful information from medical literature and building knowledge bases