# MIMIC-Sepsis: A Curated Benchmark for Modeling and Learning from Sepsis Trajectories in the ICU

arXiv: https://arxiv.org/abs/2510.24500 · HTML: https://mlsanity.com/paper/2510.24500/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Reinforcement Learning for Sepsis Treatment](https://mlsanity.com/paper/1711.09602/) (2017)
- [An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2011.11235/) (2020)

## Built on by (most cited first, 2 of 2)

- [CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction](https://mlsanity.com/paper/2604.14532/) (2026)
- [Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning](https://mlsanity.com/paper/2606.19481/) (2026)

## Abstract (arXiv)

Sepsis is a leading cause of mortality in intensive care units (ICUs), yet existing research often relies on outdated datasets, non-reproducible preprocessing pipelines, and limited coverage of clinical interventions. We introduce MIMIC-Sepsis, a curated cohort and benchmark framework derived from the MIMIC-IV database, designed to support reproducible modeling of sepsis trajectories. Our cohort includes 35,239 ICU patients with time-aligned clinical variables and standardized treatment data, including vasopressors, fluids, mechanical ventilation and antibiotics. We describe a transparent preprocessing pipeline-based on Sepsis-3 criteria, structured imputation strategies, and treatment inclusion-and release it alongside benchmark tasks focused on early mortality prediction, length-of-stay estimation, and shock onset classification. Empirical results demonstrate that incorporating treatment variables substantially improves model performance, particularly for Transformer-based architectures. MIMIC-Sepsis serves as a robust platform for evaluating predictive and sequential models in critical care research.