1 research outputs found
Modeling Drug-Disease Relations with Linguistic and Knowledge Graph Constraints
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