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.

本文介绍了一种基于监督信号的议题模型框架，该框架用于训练潜在狄利克雷分配，旨在平衡高维数据的忠实生成解释和相关类标签的准确预测，并将该框架应用于预测抑郁药物使用，相较于传统的监督议题模型和基于高维逻辑回归的药物推荐任务中，本文提出的方法表现更好。