  We describe a method for parameter estimation in bipartite probabilistic
graphical models for joint prediction of clinical conditions from the
electronic medical record. The method does not rely on the availability of
gold-standard labels, but rather uses noisy labels, called anchors, for
learning. We provide a likelihood-based objective and a moments-based
initialization that are effective at learning the model parameters. The learned
model is evaluated in a task of assigning a heldout clinical condition to
patients based on retrospective analysis of the records, and outperforms
baselines which do not account for the noisiness in the labels or do not model
the conditions jointly.
