Boosted Tree Ensembles for Predicting Postsurgical ICU Mortality

Garrick Aden-Buie, Yun Chen, Gina Romero, Rashad Kayal, Hui Yang, Ph.D.

Event INFORMS Annual Meeting
Date Monday, 7 October 2013
Location Minneapolis, MN

Abstract

Real-time monitoring of patient conditions in the ICU environment is essential in supporting clinical decisions and ensuring optimal allocation of medical resources. This study focuses on accurately predicting in-hospital ICU patient mortality utilizing functional profile data. We demonstrate that a boosted trees ensemble model is well suited for the diverse data typologies present in ICU data and provides an interpretable and accurate model to aid clinical experts in critical ICU decisions.