Semi-automated extraction of research topics and trends from NCI funding
  in radiological sciences from 2000-2020
Mark Nguyen Peter Beidler Joseph Tsai August Anderson Daniel Chen Paul Kinahan John Kang
Investigators, funders, and the public desire knowledge on topics and trends
in publicly funded research but current efforts in manual categorization are
limited in scale and understanding. We developed a semi-automated approach to
extract and name research topics, and applied this to \$1.9B of NCI funding
over 21 years in the radiological sciences to determine micro- and macro-scale
research topics and funding trends. Our method relies on sequential clustering
of existing biomedical-based word embeddings, naming using subject matter
experts, and visualization to discover trends at a macroscopic scale above
individual topics. We present results using 15 and 60 cluster topics, where we
found that 2D projection of grant embeddings reveals two dominant axes:
physics-biology and therapeutic-diagnostic. For our dataset, we found that
funding for therapeutics- and physics-based research have outpaced diagnostics-
and biology-based research, respectively. We hope these results may (1) give
insight to funders on the appropriateness of their funding allocation, (2)
assist investigators in contextualizing their work and explore neighboring
research domains, and (3) allow the public to review where their tax dollars
are being allocated.