Interpretable Patient Mortality Prediction with Multi-value Rule Sets

TL;DR

This paper introduces a Multi-value Rule Set model for predicting patient mortality that uses multi-valued rules for more concise and efficient data pattern capture, outperforming existing methods.

Contribution

It presents a novel Bayesian framework for learning multi-value rule sets, improving interpretability and efficiency over traditional single-valued rule models.

Findings

Achieves better performance than baseline hospital systems

Uses multi-value rules for more concise data representation

Reduces data collection and storage costs

Abstract

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.