Interpretable Patient Mortality Prediction with Multi-value Rule Sets
This work addresses interpretable mortality prediction for healthcare, but it appears incremental as it builds on existing rule-based methods with a generalization.
The authors tackled the problem of predicting in-hospital patient mortality by proposing a Multi-vAlue Rule Set (MRS) model, which achieved better performance than baseline methods including the current hospital system.
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.