构建首个基于Sepsis-3标准的脓毒症轨迹基准数据集，支持可复现的重症监护研究。
MIMIC-Sepsis: A Curated Benchmark for Modeling and Learning from Sepsis Trajectories in the ICU
- 基于MIMIC-IV构建35239例患者数据，统一处理临床变量与治疗记录
- 加入治疗变量后，Transformer模型在死亡预测等任务上性能显著提升
- 适合重症医学、临床预测建模及可复现性研究者使用
脓毒症是重症监护病房（ICU）的主要致死原因，但现有研究多依赖过时数据集、不可复现的预处理流程，且临床干预覆盖不足。我们提出MIMIC-Sepsis，一个从MIMIC-IV数据库衍生的整理后队列与基准框架，旨在支持脓毒症轨迹的可复现建模。该队列包含35,239名接受时间对齐的临床变量和标准化治疗数据（包括血管活性药物、液体、机械通气和抗生素）的ICU患者。我们描述了一套透明的预处理流程——基于Sepsis-3标准，结构化插补策略，并纳入治疗信息——并发布配套基准任务：早期死亡预测、住院时长估计和休克发作分类。实证结果表明，引入治疗变量能显著提升模型性能，尤其在Transformer架构中效果突出。MIMIC-Sepsis为重症监护研究中的预测与序列建模提供了可靠平台。
原文摘要 · Abstract (English)
Sepsis is a leading cause of mortality in intensive care units (ICUs), yet existing research often relies on outdated datasets, non-reproducible preprocessing pipelines, and limited coverage of clinical interventions.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.
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