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.

翻译：在这篇应用性论文中，我们探讨了重症监护病房患者何时出院的棘手开放性问题。这可以视为一个最优停时问题，但面临三项额外挑战：1）基于观测数据评估停时策略本身就是一个复杂的因果推断问题；2）复合目标是同时最小化干预时长与最大化治疗结局，但两者无法降维合并；3）变量记录会在干预终止时停止。我们的贡献有两方面：第一，我们推广了g-formula Python工具包的实现，为具有上述结构的问题提供了评估停时策略的框架，包括阳性与覆盖性检验；第二，通过完全开源的流水线，我们将该方法应用于公共ICU数据集MIMIC-IV，展示了改进现有诊疗策略的潜力。