用智能代理框架自动优化小规模临床时序数据的机器学习流程
DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data
- 通过多个专用大模型代理协作生成、验证和优化机器学习管道
- 在多种临床任务中表现优于传统AutoML，且结果更可解释
- 适合医疗AI研发者，尤其关注小样本时序数据建模场景
临床机器学习有望支持高风险医疗决策，但可靠部署常受限于数据稀缺、异构性及时间复杂性。针对此类数据构建有效机器学习流程仍耗时且易出错，现有自动化机器学习（AutoML）系统仅部分解决该问题，因其多依赖预定义空间的暴力搜索，缺乏显式推理与记忆能力。为此，我们将小规模临床数据的AutoML从穷举搜索重构为推理驱动的迭代优化。提出DoctorAgents——一种智能体框架，通过专用于生成、验证与优化的大型语言模型代理，自主构建并优化端到端机器学习管道。DoctorAgents利用自然语言反馈进行文本梯度下降，实现精准更新而不必遍历全空间。在多种临床任务上的实验表明，DoctorAgents持续优于主流AutoML基线，并生成更具任务特异性的可解释表示。
原文摘要 · Abstract (English)
Clinical machine learning (ML) has the potential to support high-stakes medical decision-making, but reliable deployment is often constrained by scarce, heterogeneous, and temporal complexity. Developing effective ML pipelines for such data remains time-consuming and error-prone, while existing automated machine learning (AutoML) systems only partially address this challenge because they largely rely on brute-force search over predefined spaces and lack explicit reasoning and memory. We therefore reformulate AutoML for small clinical data from exhaustive search to reasoning-driven refinement. We propose DoctorAgents, an agentic AI framework that autonomously constructs and optimizes end-to-end ML pipelines through specialized large language model (LLM) agents for generation, validation, and refinement. DoctorAgents backpropagates natural-language feedback through textual gradient descent to perform targeted updates without exhaustive search. Experiments across diverse clinical tasks show that DoctorAgents consistently outperforms established AutoML baselines while producing more interpretable task-specific representations.
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