Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards

TL;DR

This paper introduces a scalable, nonparametric survival analysis method, BoXHED 2.0, to develop a real-time ICU mortality warning system using time-varying EHR data from MIMIC-IV, outperforming traditional models.

Contribution

It applies BoXHED 2.0 to incorporate time-dependent covariates for real-time mortality prediction, advancing survival analysis in critical care.

Findings

Achieved AUC-PRC of 0.41

Achieved AUC-ROC of 0.83

Demonstrated benefits of real-time monitoring

Abstract

Electronic Health Record (EHR) systems provide critical, rich and valuable information at high frequency. One of the most exciting applications of EHR data is in developing a real-time mortality warning system with tools from survival analysis. However, most of the survival analysis methods used recently are based on (semi)parametric models using static covariates. These models do not take advantage of the information conveyed by the time-varying EHR data. In this work, we present an application of a highly scalable survival analysis method, BoXHED 2.0 to develop a real-time in-ICU mortality warning indicator based on the MIMIC IV data set. Importantly, BoXHED can incorporate time-dependent covariates in a fully nonparametric manner and is backed by theory. Our in-ICU mortality model achieves an AUC-PRC of 0.41 and AUC-ROC of 0.83 out of sample, demonstrating the benefit of real-time monitoring.