基于观察数据构建个体化治疗推荐框架，提升心衰患者急性肾损伤治疗效果。
From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
- 融合因果推断与目标试验范式，构建可解释的个体化治疗模型
- 在真实心衰合并急性肾损伤患者中，新方案显著优于当前治疗策略
- 适用于临床决策支持系统开发，尤其关注治疗安全与有效性验证
我们提出一种构建个体化治疗推荐模型的框架，基于近年来关于学习患者级因果模型的研究成果，并受Hernan和Robins提出的靶向试验范式启发。重点聚焦于安全性与有效性，包括使用观察性数据时的因果识别问题。本文未提供具体模型，而是提供一种将现有方法与知识整合到实用流程中的方式。进一步展示了在住院期间发生急性肾损伤的心力衰竭患者治疗优化的真实案例。结果表明，该流程可显著改善患者预后，优于当前治疗方案。
原文摘要 · Abstract (English)
We propose a framework for building patient-specific treatment recommendation models, building on the large recent literature on learning patient-level causal models and inspired by the target trial paradigm of Hernan and Robins. We focus on safety and validity, including the crucial issue of causal identification when using observational data. We do not provide a specific model, but rather a way to integrate existing methods and know-how into a practical pipeline. We further provide a real world use-case of treatment optimization for patients with heart failure who develop acute kidney injury during hospitalization. The results suggest our pipeline can improve patient outcomes over the current treatment regime.
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