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English(EN) Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models

心血管筛查模型准确性归因于数据泄露，而非模型类型

作者 PulseAugur 编辑部 · [1 个来源] ·

一项发表在arXiv上的新研究调查了心血管筛查模型的准确性，结果显示报告的高准确性主要是由于目标泄露而非模型类别。研究人员发现，仅移除两个诊断后特征就显著降低了所有测试模型的AUROC，将它们压缩到一个狭窄的性能区间。研究强调，评估实践而非模型能力是主要限制因素，并强调了透明度在审计公平性和不确定性方面的优势。 AI

影响 强调了在AI模型开发中进行稳健数据验证的关键需求，尤其是在医疗保健等敏感领域。

排序理由 该集群包含一篇发表在arXiv上的研究论文，详细介绍了对机器学习模型的新审计。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

心血管筛查模型准确性归因于数据泄露，而非模型类型

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该集群包含一篇发表在arXiv上的研究论文，详细介绍了对机器学习模型的新审计。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Raad Bin Tareaf, Murad Al-Rajab, Samia Loucif, Samer Ellaham, Cedric Schmitz ·

    目标泄露而非模型类别解释了基于调查的心血管筛查中的报告准确性：对玻璃盒模型和表格基础模型的泄露分层审计

    arXiv:2609.11838v1 Announce Type: new Abstract: Cardiovascular screening models trained on national health surveys routinely report areas under the receiver operating characteristic curve (AUROC) near 0.89. We asked whether that accuracy reflects learning or target leakage, wheth…

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