Developing safe, effective, and practically useful clinical prediction models (CPMs) traditionally requires iterative collaboration between clinical experts, data scientists, and informaticists. This process refines the often small but critical details of the model building process, such as which features/patients to include and how clinical categories should be defined. However, this traditional collaboration process is extremely time- and resource-intensive, resulting in only a small fraction of CPMs reaching clinical practice. This challenge intensifies when teams attempt to incorporate unstructured clinical notes, which can contain an enormous number of concepts. To address this challenge, we introduce HACHI, an iterative human-in-the-loop framework that uses AI agents to accelerate the development of fully interpretable CPMs by enabling the exploration of concepts in clinical notes. HACHI alternates between (i) an AI agent rapidly exploring and evaluating candidate concepts in clinical notes and (ii) clinical and domain experts providing feedback to improve the CPM learning process. HACHI defines concepts as simple yes-no questions that are used in linear models, allowing the clinical AI team to transparently review, refine, and validate the CPM learned in each round. In two real-world prediction tasks (acute kidney injury and traumatic brain injury), HACHI outperforms existing approaches, surfaces new clinically relevant concepts not included in commonly-used CPMs, and improves model generalizability across clinical sites and time periods. Furthermore, HACHI reveals the critical role of the clinical AI team, such as directing the AI agent to explore concepts that it had not previously considered, adjusting the granularity of concepts it considers, changing the objective function to better align with the clinical objectives, and identifying issues of data bias and leakage.

翻译：开发安全、有效且具有实际应用价值的临床预测模型（CPMs）传统上需要临床专家、数据科学家和信息学专家之间的迭代协作。这一过程旨在完善模型构建过程中那些通常细微但至关重要的细节，例如应纳入哪些特征/患者以及如何定义临床类别。然而，这种传统的协作过程极其耗费时间和资源，导致仅有极小比例的CPMs能够最终应用于临床实践。当团队试图纳入非结构化的临床记录时，这一挑战会进一步加剧，因为其中可能包含海量的临床概念。为应对这一挑战，我们提出了HACHI，一种迭代式人在回路框架，该框架利用AI智能体通过探索临床记录中的概念来加速开发完全可解释的CPMs。HACHI交替进行以下两个步骤：(i) AI智能体快速探索和评估临床记录中的候选概念；(ii) 临床及领域专家提供反馈以改进CPMs的学习过程。HACHI将概念定义为用于线性模型的简单是非问题，这使得临床AI团队能够透明地审查、优化和验证每一轮学习得到的CPM。在两个真实世界的预测任务（急性肾损伤和创伤性脑损伤）中，HACHI的表现优于现有方法，揭示了常用CPMs中未包含的、具有临床相关性的新概念，并提升了模型在不同临床地点和时期之间的泛化能力。此外，HACHI揭示了临床AI团队的关键作用，例如引导AI智能体探索其先前未考虑的概念、调整其考虑概念的粒度、更改目标函数以更好地与临床目标对齐，以及识别数据偏差和数据泄露问题。