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English(EN) HiMA-MDD: A Hierarchical Multi-Agent Harness for Interpretable Multimodal Depression Detection in Clinical Interviews

新的分层多智能体系统增强抑郁症检测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了HiMA-MDD，一个新颖的分层多智能体系统，用于临床访谈中的可解释多模态抑郁症检测。该系统将证据收集和评估组织成三个不同的智能体层，模仿了临床诊断的分层性质。HiMA-MDD旨在通过明确管理证据访问、评分权限和反馈循环来提高抑郁症评估的准确性和可审计性。使用Qwen2.5-72B-Instruct作为骨干进行的实验表明，HiMA-MDD在E-DAIC数据集上超越了当前最先进的方法。 AI

影响 这项研究可能带来更准确、更透明的心理健康诊断AI工具。

排序理由 该集群包含一篇详细介绍新AI系统及其实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的分层多智能体系统增强抑郁症检测能力

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该集群包含一篇详细介绍新AI系统及其实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ao Chen, Xiaojiang Peng ·

    HiMA-MDD：一种用于临床访谈中可解释的多模态抑郁症检测的分层多智能体工具

    arXiv:2608.21868v1 Announce Type: new Abstract: Depression assessment from multimodal clinical interviews requires integrating dispersed evidence from multiple symptoms into a coherent PHQ-8 profile. This process is hierarchical: relevant evidence is often sparse and context-depe…

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