Objective: Immediately after the outbreak of nCoV, many clinical trials are registered
for COVID-19. The numbers of registrations are now raising inordinately. It is challenging to understand
which research areas are explored in this massive pool of clinical studies. If such information
can be compiled, then it is easy to explore new research studies for possible contributions in
Methods: In the present work, a text-mining technique of artificial intelligence is utilized to map
the research domains explored through the clinical trials of COVID-19. With the help of the open--
source and graphical user interface-based tool, 3007 clinical trials are analyzed here. The dataset is
acquired from the international clinical trial registry platform of WHO. With the help of hierarchical
cluster analysis, the clinical trials were grouped according to their common research studies. These
clusters are analyzed manually using their word clouds for understanding the scientific area of
a particular cluster. The scientific fields of clinical studies are comprehensively reviewed and discussed
based on this analysis.
Results: More than three-thousand clinical trials are grouped in 212 clusters by hierarchical cluster
analysis. Manual intervention of these clusters using their individual word-cloud helped to identify
various scientific areas which are explored in COVID19 related clinical studies.
Conclusion: The text-mining is an easy and fastest way to explore many registered clinical trials.
In our study, thirteen major clusters or research areas were identified in which the majority of clinical
trials were registered. Many other uncategorized clinical studies were also identified as “miscellaneous
studies”. The clinical trials within the individual cluster were studied, and their research
purposes are compiled comprehensively in the present work.