  This project demonstrates how medical corpus hypothesis generation, a
knowledge discovery field of AI, can be used to derive new research angles for
landscape and urban planners. The hypothesis generation approach herein
consists of a combination of deep learning with topic modeling, a probabilistic
approach to natural language analysis that scans aggregated research databases
for words that can be grouped together based on their subject matter
commonalities; the word groups accordingly form topics that can provide
implicit connections between two general research terms. The hypothesis
generation system AGATHA was used to identify likely conceptual relationships
between emerging infectious diseases (EIDs) and deforestation, with the
objective of providing landscape planners guidelines for productive research
directions to help them formulate research hypotheses centered on deforestation
and EIDs that will contribute to the broader health field that asserts causal
roles of landscape-level issues. This research also serves as a partial
proof-of-concept for the application of medical database hypothesis generation
to medicine-adjacent hypothesis discovery.
