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Updated: Feb 22, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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通过非IID和稀缺数据场景的联合学习来加强生存分析

Patricia A Apellániz1, Juan Parras1, Santiago Zazo1

  • 1Information Processing and Telecommunications Center, ETS Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040, Madrid, Spain.

Computers in biology and medicine
|February 20, 2026
PubMed
概括

联合合成数据共享 (FedSDS) 允许使用保护隐私的合成数据进行协作生存分析. 这种方法可以提高AI模型在稀缺,异质的医疗保健数据集上的性能,而无需直接共享数据.

关键词:
数据异质性 数据异质性数据稀缺性 数据稀缺性联合学习是联合学习.幸存率分析 幸存率分析合成数据生成的合成数据生成.

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