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

arXiv: https://arxiv.org/abs/2110.08949 · HTML: https://mlsanity.com/paper/2110.08949/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 17 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [Deep Recurrent Survival Analysis](https://mlsanity.com/paper/1809.02403/) (2018)
- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates](https://mlsanity.com/paper/2006.14218/) (2020)
- [BoXHED2.0: Scalable boosting of dynamic survival analysis](https://mlsanity.com/paper/2103.12591/) (2021)

## Built on by (most cited first, 6 of 6)

- [Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking](https://mlsanity.com/paper/2302.10902/) (2023)
- [Training and Evaluating Causal Forecasting Models for Time-Series](https://mlsanity.com/paper/2411.00126/) (2024)
- [Temporal Gaussian Copula For Clinical Multivariate Time Series Data Imputation](https://mlsanity.com/paper/2504.02317/) (2025)
- [A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data](https://mlsanity.com/paper/2509.10517/) (2025)
- [QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction](https://mlsanity.com/paper/2608.06294/) (2026)
- [Random Hazard Forests](https://mlsanity.com/paper/2608.21597/) (2026)

## Abstract (arXiv)

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.