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Interpretable Patient Mortality Prediction with Multi-value Rule Sets

Publication ,  Journal Article
Wang, T; Allareddy, V; Rampa, S; Allareddy, V
July 6, 2018

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.

Duke Scholars

Publication Date

July 6, 2018
 

Citation

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Wang, T., Allareddy, V., & Rampa, S. (2018). Interpretable Patient Mortality Prediction with Multi-value Rule Sets.
Wang, Tong, Veerajalandhar Allareddy, Sankeerth Rampa, and Veerasathpurush Allareddy. “Interpretable Patient Mortality Prediction with Multi-value Rule Sets,” July 6, 2018.

Publication Date

July 6, 2018