  Nowadays, the interpretability of machine learning models is becoming
increasingly important, especially in the medical domain. Aiming to shed some
light on how to rationalize medical relation prediction, we present a new
interpretable framework inspired by existing theories on how human memory
works, e.g., theories of recall and recognition. Given the corpus-level
statistics, i.e., a global co-occurrence graph of a clinical text corpus, to
predict the relations between two entities, we first recall rich contexts
associated with the target entities, and then recognize relational interactions
between these contexts to form model rationales, which will contribute to the
final prediction. We conduct experiments on a real-world public clinical
dataset and show that our framework can not only achieve competitive predictive
performance against a comprehensive list of neural baseline models, but also
present rationales to justify its prediction. We further collaborate with
medical experts deeply to verify the usefulness of our model rationales for
clinical decision making.
