AI 中文总结
该研究针对临床表格数据中学到的患者表征是否保留原始数据相似性的问题，提出结合贝叶斯优化权重的对应分数，经实验表明表格基础模型的表征保留对应关系更优，可用于评估表征质量并辅助方法与维度选择。
AI 中文摘要
识别具有相似临床特征的患者对队列发现、临床决策支持和个性化医学至关重要，但目前尚不清楚学到的患者表征是否保留了原始临床数据中存在的相似性。我们提出一种对应分数，以独立于标记结局直接评估这些关系的保留程度，该分数结合了互补的关系保留度量，并通过贝叶斯优化估计特定于数据集的权重。我们在四个全血细胞计数数据集上评估了该方法，使用了五个表格基础模型和六种经典变换方法生成的表征。不同方法和表征维度下的对应关系存在显著差异；患者关系通常在中间维度下保留得最佳，而更强的降维或扩展会引入与原始数据结构的更大偏差。基础模型比经典方法更一致地保留了对应关系，改进幅度约为5%至超过125%。所提出的分数为下游使用前评估表征质量提供了一种方法，并支持对表征方法和维度的明智选择。
英文摘要
Identifying patients with similar clinical characteristics is important for cohort discovery, clinical decision support, and personalized medicine. However, it is unclear whether learned patient representations preserve the similarities present in the original clinical data. We propose a correspondence score to directly assess how well these relationships are preserved, independently of labeled outcomes. The score combines complementary relationship-preservation metrics, with dataset-specific weights estimated through Bayesian optimization. We evaluated the approach on four complete blood count datasets using representations generated by five tabular foundation models and six classical transformation methods. Correspondence differed substantially across methods and representation dimensionalities. Patient relationships were generally best preserved at intermediate dimensionalities, while stronger reduction or expansion introduced greater deviations from the original data structure. Foundation models preserved correspondence more consistently than classical methods, with improvements ranging from approximately 5% to over 125%. The proposed score provides a way to evaluate representation quality before downstream use and supports informed selection of representation methods and dimensionalities.
