  As patients' access to their doctors' clinical notes becomes common,
translating professional, clinical jargon to layperson-understandable language
is essential to improve patient-clinician communication. Such translation
yields better clinical outcomes by enhancing patients' understanding of their
own health conditions, and thus improving patients' involvement in their own
care. Existing research has used dictionary-based word replacement or
definition insertion to approach the need. However, these methods are limited
by expert curation, which is hard to scale and has trouble generalizing to
unseen datasets that do not share an overlapping vocabulary. In contrast, we
approach the clinical word and sentence translation problem in a completely
unsupervised manner. We show that a framework using representation learning,
bilingual dictionary induction and statistical machine translation yields the
best precision at 10 of 0.827 on professional-to-consumer word translation, and
mean opinion scores of 4.10 and 4.28 out of 5 for clinical correctness and
layperson readability, respectively, on sentence translation. Our
fully-unsupervised strategy overcomes the curation problem, and the clinically
meaningful evaluation reduces biases from inappropriate evaluators, which are
critical in clinical machine learning.
