深度因果行为策略学习：在医疗保健中的应用
Deep Causal Behavioral Policy Learning: Applications to Healthcare
AI总结：
本文提出深度因果行为策略学习（DC-BPL）方法，利用基于transformer的大型临床行为模型（LCBM）学习临床动作路径分布并识别其与患者结果的因果联系，从而确定最优医疗服务提供者并模拟其护理决策。
摘要：
我们提出了一种基于深度学习的方法，用于研究多样化非随机化医疗环境中的动态临床行为策略。我们提出的方法论——深度因果行为策略学习（DC-BPL）——使用深度学习算法来学习高维临床动作路径的分布，并识别这些动作路径与患者结果之间的因果联系。具体而言，我们的方法：(1) 识别医疗服务提供者分配对临床结果的因果效应；(2) 学习给定提供者在不断变化的患者信息下会采取的临床动作分布；(3) 结合这些步骤来识别给定患者类型的最佳提供者并模拟该提供者的护理决策。在这一策略的基础上，我们使用 transformer 架构在电子健康记录数据上训练了一个大型临床行为模型（LCBM），并展示了其估计临床行为策略的能力。我们提出了一种对使用 LCBM 学习到的行为策略的新颖解释：它是对用于治疗患者的复杂且通常是隐性的知识的高效编码。这使我们能够学习到一个对广泛医疗保健应用至关重要的策略空间，在这些应用中，绝大多数临床知识是通过多年实践隐性获得的，而与患者护理相关的信息只有极小一部分被记录下来（例如在教科书、研究或标准化指南中）。
英文摘要：
We present a deep learning-based approach to studying dynamic clinical behavioral regimes in diverse non-randomized healthcare settings. Our proposed methodology - deep causal behavioral policy learning (DC-BPL) - uses deep learning algorithms to learn the distribution of high-dimensional clinical action paths, and identifies the causal link between these action paths and patient outcomes. Specifically, our approach: (1) identifies the causal effects of provider assignment on clinical outcomes; (2) learns the distribution of clinical actions a given provider would take given evolving patient information; (3) and combines these steps to identify the optimal provider for a given patient type and emulate that provider's care decisions. Underlying this strategy, we train a large clinical behavioral model (LCBM) on electronic health records data using a transformer architecture, and demonstrate its ability to estimate clinical behavioral policies. We propose a novel interpretation of a behavioral policy learned using the LCBM: that it is an efficient encoding of complex, often implicit, knowledge used to treat a patient. This allows us to learn a space of policies that are critical to a wide range of healthcare applications, in which the vast majority of clinical knowledge is acquired tacitly through years of practice and only a tiny fraction of information relevant to patient care is written down (e.g. in textbooks, studies or standardized guidelines).
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。
