  The primary goal of drug safety researchers and regulators is to promptly
identify adverse drug reactions. Doing so may in turn prevent or reduce the
harm to patients and ultimately improve public health. Evaluating and
monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever
growing collection of spontaneous reports from health professionals,
physicians, and pharmacists, and information voluntarily submitted by patients.
In this scenario, facilitating analysis of such reports via automation has the
potential to rapidly identify safety signals. Unfortunately, public resources
for developing natural language models for this task are scant. We present
PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated
events from medical case reports and biomedical literature, making it the
largest such public dataset to date. We describe the hierarchical event schema
designed to provide coarse and fine-grained information about patients'
demographics, treatments and (side) effects. Along with the discussion of the
dataset, we present a thorough experimental evaluation of current
state-of-the-art approaches for biomedical event extraction, point out their
limitations, and highlight open challenges to foster future research in this
area.
