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Updated: May 16, 2025

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使用真实世界的数据和机器学习来识别临床结果的预测性亚现象型

Weishen Pan1, Deep Hathi2, Zhenxing Xu1

  • 1Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.

Nature communications
|May 12, 2025
PubMed
概括

这项研究引入了图形编码混合物生存率 (GEMS),以确定患者亚表型,以预测高级非小细胞肺癌 (aNSCLC) 治疗反应. GEMS改善了整体存活率 (OS) 的预测,并揭示了个性化治疗的不同患者群体.

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