AutoHealth自主建模健康数据，兼顾准确率与不确定性评估。
AutoHealth: An Uncertainty-Aware Multi-Agent System for Autonomous Health Data Modeling
- 五类智能体闭环协作，自动生成模型并评估可信度
- 在17个真实任务中预测性能提升29.2%，不确定性估计提升50.2%
- 适合需高可靠性决策的医疗AI场景
基于大语言模型的智能体在自主机器学习方面展现出巨大潜力，但在健康数据领域的应用仍受限。现有系统难以泛化于异构健康数据模态，过度依赖预设解决方案模板，且缺乏对不确定性估计的关注，而后者对医疗可靠决策至关重要。为此，我们提出 extit{AutoHealth}——一种新型的不确定性感知多智能体系统，可自主建模健康数据并评估模型可靠性。该系统通过五个专业智能体的闭环协作，完成数据探索、任务导向的模型构建、训练与优化，并同时优化预测性能与不确定性量化。除生成可用模型外，系统还输出全面报告，支持可信解释与风险敏感决策。为严格评估其有效性，我们构建了一个包含17个跨数据模态与学习场景的真实世界基准。AutoHealth成功完成所有任务，在预测性能上优于最先进基线29.2%，在不确定性估计上提升50.2%。
原文摘要 · Abstract (English)
LLM-based agents have demonstrated strong potential for autonomous machine learning, yet their applicability to health data remains limited. Existing systems often struggle to generalize across heterogeneous health data modalities, rely heavily on predefined solution templates with insufficient adaptation to task-specific objectives, and largely overlook uncertainty estimation, which is essential for reliable decision-making in healthcare. To address these challenges, we propose \textit{AutoHealth}, a novel uncertainty-aware multi-agent system that autonomously models health data and assesses model reliability. \textit{AutoHealth} employs closed-loop coordination among five specialized agents to perform data exploration, task-conditioned model construction, training, and optimization, while jointly prioritizing predictive performance and uncertainty quantification. Beyond producing ready-to-use models, the system generates comprehensive reports to support trustworthy interpretation and risk-aware decision-making. To rigorously evaluate its effectiveness, we curate a challenging real-world benchmark comprising 17 tasks across diverse data modalities and learning settings. \textit{AutoHealth} completes all tasks and outperforms state-of-the-art baselines by 29.2\% in prediction performance and 50.2\% in uncertainty estimation.
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