SCOPE-AD：基于能量模型的序贯成本感知序数信念规划用于诊断智能体

中文摘要 AI 辅助

阿尔茨海默病（AD）的诊断需要在异质的测试成本和患者负担下进行序贯的证据获取。固定模态的预测器无法联合决定应获取哪项测试，或何时现有证据足以进行诊断。我们提出了SCOPE-AD（基于能量模型的序贯成本感知序数信念规划用于诊断智能体），用于对认知正常（CN）、轻度认知障碍（MCI）和AD病例进行成本感知分类。一个掩码感知的序数模型沿有序的CN–MCI–AD连续体表示不确定性。回顾性训练记录为基于能量的教师模型提供采样的Bellman目标，其动作分布被蒸馏到Qwen策略中。在部署时，智能体在可用性和预算约束下选择获取或诊断动作，且无法访问未获取的值。每次获取后，证据和序数信念在下一个决策前更新。在ADNI上，SCOPE-AD以平均获取成本50.46美元实现了77.70%的Macro-F1，比最强评估基线高出9.34个百分点。全模态评估仅将Macro-F1提高1.89个百分点，同时使获取成本增加116.7倍。这些结果支持选择性获取以实现成本效益高的诊断。

英文摘要

Alzheimer's disease (AD) diagnosis requires sequential evidence acquisition under heterogeneous test costs and patient burden. Fixed-modality predictors do not jointly decide which test to acquire or when the available evidence is sufficient for diagnosis. We propose SCOPE-AD (Sequential Cost-Aware Ordinal-Belief Planning with Energy-Based Models for Diagnostic Agents) for cost-aware classification of cognitively normal (CN), mild cognitive impairment (MCI), and AD cases. A mask-aware ordinal model represents uncertainty along the ordered CN--MCI--AD continuum. Retrospective training records provide sampled Bellman targets for an energy-based teacher, whose action distributions are distilled into a Qwen policy. At deployment, the agent selects acquisition or diagnosis actions under availability and budget constraints without access to unacquired values. After each acquisition, the evidence and ordinal belief are updated before the next decision. On ADNI, SCOPE-AD achieves 77.70\% Macro-F1 at an average acquisition cost of \$50.46, exceeding the strongest evaluated baseline by 9.34 percentage points. Full-modality evaluation raises Macro-F1 by only 1.89 points while increasing acquisition cost by 116.7 times. These results support selective acquisition for cost-effective diagnosis.

发表机构

• University of Bristol（布里斯托大学）
• Imperial College London（帝国理工学院）
• Manchester Metropolitan University（曼彻斯特城市大学）
• Hunan Normal University（湖南师范大学）

机构由 AI 辅助整理，请以论文原文为准。

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5 pages,2 figures