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We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predictingpatient mortality. Compared to rule sets built from single-valued rules, MRSadopts a more generalized form of association rules that allows multiple valuesin a condition. Rules of this form are more concise than classicalsingle-valued rules in capturing and describing patterns in data. Ourformulation also pursues a higher efficiency of feature utilization, whichreduces possible cost in data collection and storage. We propose a Bayesianframework for formulating a MRS model and propose an efficient inference methodfor learning a maximum \emph{a posteriori}, incorporating theoreticallygrounded bounds to iteratively reduce the search space and improve the searchefficiency. Experiments show that our model was able to achieve betterperformance than baseline method including the current system used by thehospital.

Interpretable Patient Mortality Prediction with Multi-value Rule Sets