With recent achievements in tasks requiring context awareness, foundation models have been adopted to treat large-scale data from electronic health record (EHR) systems. However, previous clinical recommender systems based on foundation models have a limited purpose of imitating clinicians' behavior and do not directly consider a problem of missing values. In this paper, we propose Clinical Decision Transformer (CDT), a recommender system that generates a sequence of medications to reach a desired range of clinical states given as goal prompts. For this, we conducted goal-conditioned sequencing, which generated a subsequence of treatment history with prepended future goal state, and trained the CDT to model sequential medications required to reach that goal state. For contextual embedding over intra-admission and inter-admissions, we adopted a GPT-based architecture with an admission-wise attention mask and column embedding. In an experiment, we extracted a diabetes dataset from an EHR system, which contained treatment histories of 4788 patients. We observed that the CDT achieved the intended treatment effect according to goal prompt ranges (e.g., NormalA1c, LowerA1c, and HigherA1c), contrary to the case with behavior cloning. To the best of our knowledge, this is the first study to explore clinical recommendations from the perspective of goal prompting. See https://clinical-decision-transformer.github.io for code and additional information.

翻译：随着上下文感知任务取得最新进展，基础模型已被用于处理电子健康记录系统的大规模数据。然而，基于基础模型的临床推荐系统存在模仿临床医生行为的局限性，且未直接考虑缺失值问题。本文提出临床决策转换器（CDT）——一种能够根据目标提示生成用药序列以达预期临床状态范围的推荐系统。为此，我们设计了目标条件序列生成方法：通过预置未来目标状态生成治疗历史子序列，训练CDT建模达成目标状态所需的序贯用药方案。为实现入院内与跨入院阶段的上下文嵌入，我们采用基于GPT的架构，结合入院级注意力掩码与列嵌入。实验中从EHR系统提取包含4788名患者治疗史的糖尿病数据集，观察到与行为克隆不同，CDT能根据目标提示范围（如正常糖化血红蛋白、低糖化血红蛋白、高糖化血红蛋白）实现预期的治疗效果。据我们所知，这是首个从目标提示视角探索临床推荐的研究。代码和补充信息见https://clinical-decision-transformer.github.io。