FDA drug labels are rich sources of information about drugs and drug-disease relations, but their complexity makes them challenging texts to analyze in isolation. To overcome this, we situate these labels in two health knowledge graphs: one built from precise structured information about drugs and diseases, and another built entirely from a database of clinical narrative texts using simple heuristic methods. We show that Probabilistic Soft Logic models defined over these graphs are superior to text-only and relation-only variants, and that the clinical narratives graph delivers exceptional results with little manual effort. Finally, we release a new dataset of drug labels with annotations for five distinct drug-disease relations.
@article{arxiv.1904.00313,
title = {Modeling Drug-Disease Relations with Linguistic and Knowledge Graph Constraints},
author = {Bruno Godefroy and Christopher Potts},
journal= {arXiv preprint arXiv:1904.00313},
year = {2019}
}