Supervised topic models for clinical interpretability

By Michael C. Hughes, Huseyin Melih Elibol, Thomas H. McCoy Jr., Roy H. Perlis, Finale Doshi-Velez. 2016

9 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, General Social Sciences

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Citations in the corpus

SmoothGrad: removing noise by adding noise (2017, background): “From health care domains (Hughes et al., 2016; Doshi-Velez et al., 2014; Lou et al., 2012) to education (Kim et al., 2015), there are many domains where interpretability is important.”
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018, methodology): “…al., 2015a, R¨uping, 2006, Bucilu et al., 2006, Ustun and Rudin, 2016, Doshi-Velez et al., 2015, Kim et al., 2015b, Krakovna and Doshi-Velez, 2016, Hughes et al., 2016, Jung et al., 2017], various kinds of logic-based methods [Wang and Rudin, 2015, Lakkaraju et al., 2016, Singh et al., 2016, Liu…”
xGEMs: Generating Examplars to Explain Black-Box Models (2018, background): “In this work, we posit that there need not be an inherent tradeoff between model performance and explanability, as is generally assumed (and found in [22, 16, 20]).”