  Clinical notes are an efficient way to record patient information but are
notoriously hard to decipher for non-experts. Automatically simplifying medical
text can empower patients with valuable information about their health, while
saving clinicians time. We present a novel approach to automated simplification
of medical text based on word frequencies and language modelling, grounded on
medical ontologies enriched with layman terms. We release a new dataset of
pairs of publicly available medical sentences and a version of them simplified
by clinicians. Also, we define a novel text simplification metric and
evaluation framework, which we use to conduct a large-scale human evaluation of
our method against the state of the art. Our method based on a language model
trained on medical forum data generates simpler sentences while preserving both
grammar and the original meaning, surpassing the current state of the art.
