在结构缺失条件下利用网络嵌入进行迁移学习
Transfer Learning with Network Embeddings under Structured Missingness
AI总结：
TransNEST通过整合图形数据和先验组结构，在结构缺失条件下提升跨站点迁移学习的性能。
摘要：
现代数据驱动应用越来越多地依赖于跨多个站点收集的大型异构数据集。数据可用性、特征表示和底层人群的差异往往导致结构缺失，这使得从数据丰富的环境向数据有限的环境转移信息变得更加复杂。许多迁移学习方法忽略了这种结构，限制了它们在不同站点之间捕捉有意义关系的能力。我们提出了TransNEST（在结构缺失条件下利用网络嵌入进行迁移学习），一个框架，它整合了源和目标站点的图形数据以及先验组结构，以构建和优化网络嵌入。TransNEST能够处理站点特定特征，适应性地捕捉组内异质性和站点间差异，并在部分特征重叠的情况下改进嵌入估计。我们建立了TransNEST估计器的收敛速度，并在模拟中展示了强大的有限样本性能。我们将TransNEST应用于多站点电子健康记录研究，将特征嵌入从通用医院系统转移到儿童医院系统。使用层次本体结构，TransNEST提高了儿童嵌入并支持更准确的儿童知识提取，实现了在识别儿童特定的关系特征对方面比基准方法最好的准确性。
英文摘要：
Modern data-driven applications increasingly rely on large, heterogeneous datasets collected across multiple sites. Differences in data availability, feature representation, and underlying populations often induce structured missingness, complicating efforts to transfer information from data-rich settings to those with limited data. Many transfer learning methods overlook this structure, limiting their ability to capture meaningful relationships across sites. We propose TransNEST (Transfer learning with Network Embeddings under STructured missingness), a framework that integrates graphical data from source and target sites with prior group structure to construct and refine network embeddings. TransNEST accommodates site-specific features, captures within-group heterogeneity and between-site differences adaptively, and improves embedding estimation under partial feature overlap. We establish the convergence rate for the TransNEST estimator and demonstrate strong finite-sample performance in simulations. We apply TransNEST to a multi-site electronic health record study, transferring feature embeddings from a general hospital system to a pediatric hospital system. Using a hierarchical ontology structure, TransNEST improves pediatric embeddings and supports more accurate pediatric knowledge extraction, achieving the best accuracy for identifying pediatric-specific relational feature pairs compared with benchmark methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
