一种基于扩散的方法用于学习医疗处理的多结局分布
A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments
AI总结：
本文提出基于扩散的 DIME 方法，通过因果掩码和自回归生成学习医疗处理多结局的联合干预分布，支持混合类型结局与不确定性量化，是首个面向该任务的神经方法。
摘要：
在医学中，治疗通常会影响多个相互依赖的结局，例如主要终点、并发症、不良事件或其他次要终点。因此，为了做出最优治疗决策，临床医生希望学习多维治疗结局的分布。然而，绝大多数用于预测治疗效果的机器学习方法都集中在单结局场景，尽管医学数据通常包含多个相互依赖的结局。为了解决这一局限，我们提出了一种新颖的基于扩散的方法，称为 DIME，用于学习医疗处理多个结局的联合分布。我们解决了医疗实践中相关的三个挑战：（i）该方法专门用于学习多个医学结局的联合干预分布，从而能够在具有不确定性量化的条件下进行可靠决策，而不是仅仅依赖点估计；（ii）它显式地捕捉结局之间的依赖结构；（iii）它可以处理混合类型的结局，包括二值、分类和连续变量。在 DIME 中，我们通过因果掩码考虑了因果推断的基本问题。在训练方面，我们的方法将联合分布分解为一系列条件分布，并使用定制的条件掩码来考虑结局之间的依赖结构。在推断方面，我们的方法以自回归方式生成预测。这使得我们的方法能够超越因果量的点估计，从而学习联合干预分布。据我们所知，DIME 是第一个专门用于学习医疗处理联合多结局分布的神经方法。在各种实验中，我们证明了我们的方法能够有效地学习联合分布，并捕捉多个结局之间的共享信息。
英文摘要：
In medicine, treatments often influence multiple, interdependent outcomes, such as primary endpoints, complications, adverse events, or other secondary endpoints. Hence, to make optimal treatment decisions, clinicians are interested in learning the distribution of multi-dimensional treatment outcomes. However, the vast majority of machine learning methods for predicting treatment effects focus on single-outcome settings, despite the fact that medical data often include multiple, interdependent outcomes. To address this limitation, we propose a novel diffusion-based method called DIME to learn the joint distribution of multiple outcomes of medical treatments. We addresses three challenges relevant in medical practice: (i)it is tailored to learn the joint interventional distribution of multiple medical outcomes, which enables reliable decision-making with uncertainty quantification rather than relying solely on point estimates; (ii)it explicitly captures the dependence structure between outcomes; (iii)it can handle outcomes of mixed type, including binary, categorical, and continuous variables. In DIME, we take into account the fundamental problem of causal inference through causal masking. For training, our method decomposes the joint distribution into a series of conditional distributions with a customized conditional masking to account for the dependence structure across outcomes. For inference, our method auto-regressively generates predictions. This allows our method to move beyond point estimates of causal quantities and thus learn the joint interventional distribution. To the best of our knowledge, DIME is the first neural method tailored to learn the joint, multi-outcome distribution of medical treatments. Across various experiments, we demonstrate that our method effectively learns the joint distribution and captures shared information among multiple outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
