Interpretable Patient Mortality Prediction with Multi-value Rule Sets
  We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting
patient mortality. Compared to rule sets built from single-valued rules, MRS
adopts a more generalized form of association rules that allows multiple values
in a condition. Rules of this form are more concise than classical
single-valued rules in capturing and describing patterns in data. Our
formulation also pursues a higher efficiency of feature utilization, which
reduces possible cost in data collection and storage. We propose a Bayesian
framework for formulating a MRS model and propose an efficient inference method
for learning a maximum \emph{a posteriori}, incorporating theoretically
grounded bounds to iteratively reduce the search space and improve the search
efficiency. Experiments show that our model was able to achieve better
performance than baseline method including the current system used by the
hospital.
