结合不确定性量化，为临床时间序列提供成本约束下的个性化治疗推荐
Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series
- 用反事实估计+不确定性量化联合优化治疗选择
- 在心血管和新冠模拟数据上均提升治疗推荐可靠性
- 适合需兼顾疗效与预算的临床决策场景
在个性化医疗中，预测并优化不同时间框架下的治疗效果至关重要，同时在特定预算约束下选择成本效益高的治疗也极为关键。尽管反事实轨迹估计取得进展，但其与最优治疗选择之间的直接关联仍缺失。本文提出一种新方法，将反事实估计与不确定性量化相结合，推荐符合预设成本约束的个性化治疗方案。该方法独特之处在于处理连续治疗变量，并通过不确定性量化提升预测可靠性。我们在两个模拟数据集上验证了该方法：一个聚焦心血管系统，另一个针对新冠。结果表明，该方法在多种反事实估计基线中表现稳健，引入不确定性量化后，现有基线在寻找更可靠、更准确的治疗选择方面均有提升。该方法在不同设置下的鲁棒性凸显其在个性化医疗解决方案中的广泛应用潜力。
原文摘要 · Abstract (English)
In personalized medicine, the ability to predict and optimize treatment outcomes across various time frames is essential. Additionally, the ability to select cost-effective treatments within specific budget constraints is critical. Despite recent advancements in estimating counterfactual trajectories, a direct link to optimal treatment selection based on these estimates is missing. This paper introduces a novel method integrating counterfactual estimation techniques and uncertainty quantification to recommend personalized treatment plans adhering to predefined cost constraints. Our approach is distinctive in its handling of continuous treatment variables and its incorporation of uncertainty quantification to improve prediction reliability. We validate our method using two simulated datasets, one focused on the cardiovascular system and the other on COVID-19. Our findings indicate that our method has robust performance across different counterfactual estimation baselines, showing that introducing uncertainty quantification in these settings helps the current baselines in finding more reliable and accurate treatment selection. The robustness of our method across various settings highlights its potential for broad applicability in personalized healthcare solutions.
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