Reinforcement Learning in Healthcare is typically concerned with narrow self-contained tasks such as sepsis prediction or anesthesia control. However, previous research has demonstrated the potential of generalist models (the prime example being Large Language Models) to outperform task-specific approaches due to their capability for implicit transfer learning. To enable training of foundation models for Healthcare as well as leverage the capabilities of state of the art Transformer architectures, we propose the paradigm of Healthcare as Sequence Modeling, in which interaction between the patient and the healthcare provider is represented as an event stream and tasks like diagnosis and treatment selection are modeled as prediction of future events in the stream. To explore this paradigm experimentally we develop MIMIC-SEQ, a sequence modeling benchmark derived by translating heterogenous clinical records from MIMIC-IV dataset into a uniform event stream format, train a baseline model and explore its capabilities.

翻译：医疗领域的强化学习通常专注于狭窄的自包含任务，例如脓毒症预测或麻醉控制。然而，先前的研究已展示了通用模型（典型代表为大语言模型）因具备隐式迁移学习能力而可能超越任务特定方法。为支持医疗基础模型的训练并充分利用先进Transformer架构的能力，我们提出了"医疗作为序列建模"的范式，其中患者与医疗服务提供者之间的交互被表示为事件流，而诊断和治疗选择等任务则建模为对事件流中未来事件的预测。为实验性探索这一范式，我们开发了MIMIC-SEQ——一个通过将MIMIC-IV数据集中的异质性临床记录转换为统一事件流格式而构建的序列建模基准，训练了一个基线模型并探索其能力。