  Supervised topic models can help clinical researchers find interpretable
cooccurence patterns in count data that are relevant for diagnostics. However,
standard formulations of supervised Latent Dirichlet Allocation have two
problems. First, when documents have many more words than labels, the influence
of the labels will be negligible. Second, due to conditional independence
assumptions in the graphical model the impact of supervised labels on the
learned topic-word probabilities is often minimal, leading to poor predictions
on heldout data. We investigate penalized optimization methods for training
sLDA that produce interpretable topic-word parameters and useful heldout
predictions, using recognition networks to speed-up inference. We report
preliminary results on synthetic data and on predicting successful
anti-depressant medication given a patient's diagnostic history.
