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English(EN) StochIKM: Automated High-Fidelity Reconstruction of Individual Patient Data from Published Kaplan-Meier Curves

新算法从医学图表中重建患者数据

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 StochIKM，这是一种新颖的算法，旨在从已发布的 Kaplan-Meier 曲线重建个体患者数据。该方法利用了具有视觉能力的语言模型来数字化图表，并应用了明确的保真度约束，确保重建的数据能够准确地重现风险表，在图像分辨率内符合数字化曲线，并尊重打印的中位数值、区间和事件计数。StochIKM 保证如果图表被正确读取，则解决方案存在。在涉及已发布的肿瘤学研究中 79 个臂的试验中，与 LLM 改进的 IPDfromKM 替代方案相比，它在重现已发布的风险表和中位数方面表现出更高的准确性。 AI

影响 该方法可以通过实现更强大的二次分析来提高已发布医学研究的可访问性和实用性。

排序理由 该集群包含一篇详细介绍从科学图表中进行数据重建新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新算法从医学图表中重建患者数据

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该集群包含一篇详细介绍从科学图表中进行数据重建新算法的研究论文。[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) · Liangze Ke, Daniel Zelterman, Wei Wei, Guannan Gong ·

    StochIKM: Automated High-Fidelity Reconstruction of Individual Patient Data from Published Kaplan-Meier Curves

    arXiv:2610.29122v2 Announce Type: new Abstract: Individual patient data are the gold standard for secondary analysis of time-to-event evidence, but they are rarely released, and reconstruction from a published Kaplan-Meier figure could help. Existing methods all claim high fideli…

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