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).

翻译：本文提出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机制。我们提出的方法——深度因果行为策略学习（DC-BPL）——利用深度学习算法学习高维临床行为路径的分布，并识别这些行为路径与患者结果之间的因果关系。具体而言，我们的方法：（1）识别医疗提供者分配对临床结果的因果效应；（2）学习特定医疗提供者在患者信息动态变化条件下可能采取的临床行为分布；（3）整合上述步骤以确定针对特定患者类型的最优医疗提供者，并模拟该提供者的诊疗决策。基于此策略，我们使用Transformer架构在电子健康记录数据上训练大型临床行为模型（LCBM），并论证其估计临床行为策略的能力。我们对LCBM学习得到的行为策略提出一种新颖的解读：它是对治疗患者所使用的复杂且通常隐性的知识的高效编码。这使我们能够学习对广泛医疗应用至关重要的策略空间——其中绝大部分临床知识是通过多年实践默会获得的，仅有极小部分与患者护理相关的信息被书面记录（例如在教科书、研究或标准化指南中）。