用因果框架评估重症患者出院时机，兼顾治疗时长与预后效果。
A Causal Framework for Evaluating ICU Discharge Strategies
- 基于g-formula方法构建可评估停药策略的因果框架
- 在MIMIC-IV数据上验证新策略可优化当前临床实践
- 开源工具支持复现，适合医疗决策研究者使用
本文针对重症监护室（ICU）患者何时出院这一复杂临床问题，提出一种因果评估框架。该问题属于最优停止场景，面临三大挑战：一、从观察性数据中评估停药策略需解决复杂的因果推断问题；二、目标为最小化干预时长并最大化临床结局，二者无法合并为单一指标；三、干预终止后变量记录也停止。本文贡献包括：第一，扩展g-formula Python包实现，提供适用于此类结构的策略评估框架，包含正则性和覆盖率检验；第二，通过完全开源流程应用于MIMIC-IV公开数据集，证明新策略在实际应用中优于现有方案。
原文摘要 · Abstract (English)
In this applied paper, we address the difficult open problem of when to discharge patients from the Intensive Care Unit. This can be conceived as an optimal stopping scenario with three added challenges: 1) the evaluation of a stopping strategy from observational data is itself a complex causal inference problem, 2) the composite objective is to minimize the length of intervention and maximize the outcome, but the two cannot be collapsed to a single dimension, and 3) the recording of variables stops when the intervention is discontinued. Our contributions are two-fold. First, we generalize the implementation of the g-formula Python package, providing a framework to evaluate stopping strategies for problems with the aforementioned structure, including positivity and coverage checks. Second, with a fully open-source pipeline, we apply this approach to MIMIC-IV, a public ICU dataset, demonstrating the potential for strategies that improve upon current care.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。