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English(EN) DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data

DoctorAgents 框架使用 LLM 代理优化临床数据 AutoML · 跟踪 2 个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出 DoctorAgents，一个旨在优化临床时间序列数据自动化机器学习 (AutoML) 流水线的新型人工智能框架。该框架利用专门的大型语言模型 (LLM) 代理自主构建和优化机器学习流水线，超越了传统的暴力搜索方法。DoctorAgents 采用自然语言反馈和文本梯度下降进行有针对性的更新，在各种临床任务上展示出比现有 AutoML 基线更优越的性能和可解释性。 AI

影响 该框架通过提高 AutoML 的效率和可解释性，有望简化医疗保健领域关键人工智能工具的开发。

排序理由 该集群包含一篇详细介绍新人工智能框架的研究论文。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

DoctorAgents 框架使用 LLM 代理优化临床数据 AutoML · 跟踪 2 个来源

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该集群包含一篇详细介绍新人工智能框架的研究论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ruilin Wang, Bo-Hong Wang, Elizabeth Kourbatski, Jun Bai, Hegang Chen, Ziyang Song, Gilles Boire, Marie Hudson, Yue Li ·

    DoctorAgents：一个迭代优化小型临床时间序列数据的 AutoML 流水线的代理框架

    arXiv:2608.05375v1 Announce Type: new Abstract: 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 s…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Yue Li ·

    DoctorAgents：一个迭代优化小型临床时间序列数据的 AutoML 流水线的代理框架

    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,…

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