  Modern electronic health records (EHRs) provide data to answer clinically
meaningful questions. The growing data in EHRs makes healthcare ripe for the
use of machine learning. However, learning in a clinical setting presents
unique challenges that complicate the use of common machine learning
methodologies. For example, diseases in EHRs are poorly labeled, conditions can
encompass multiple underlying endotypes, and healthy individuals are
underrepresented. This article serves as a primer to illuminate these
challenges and highlights opportunities for members of the machine learning
community to contribute to healthcare.
