  We present a new corpus comprising annotations of medical entities in case
reports, originating from PubMed Central's open access library. In the case
reports, we annotate cases, conditions, findings, factors and negation
modifiers. Moreover, where applicable, we annotate relations between these
entities. As such, this is the first corpus of this kind made available to the
scientific community in English. It enables the initial investigation of
automatic information extraction from case reports through tasks like Named
Entity Recognition, Relation Extraction and (sentence/paragraph) relevance
detection. Additionally, we present four strong baseline systems for the
detection of medical entities made available through the annotated dataset.
