  Supervisory signals can help topic models discover low-dimensional data
representations that are more interpretable for clinical tasks. We propose a
framework for training supervised latent Dirichlet allocation that balances two
goals: faithful generative explanations of high-dimensional data and accurate
prediction of associated class labels. Existing approaches fail to balance
these goals by not properly handling a fundamental asymmetry: the intended task
is always predicting labels from data, not data from labels. Our new
prediction-constrained objective trains models that predict labels from heldout
data well while also producing good generative likelihoods and interpretable
topic-word parameters. In a case study on predicting depression medications
from electronic health records, we demonstrate improved recommendations
compared to previous supervised topic models and high- dimensional logistic
regression from words alone.
