We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient data. To solve this problem, we propose a supervised contrastive learning framework that learns an embedding representation for each time step of a patient time series. Our framework learns the embedding space to have the following properties: (1) nearby points in the embedding space have similar predicted class probabilities, (2) adjacent time steps of the same time series map to nearby points in the embedding space, and (3) time steps with very different raw feature vectors map to far apart regions of the embedding space. To achieve property (3), we employ a nearest neighbor pairing mechanism in the raw feature space. This mechanism also serves as an alternative to data augmentation, a key ingredient of contrastive learning, which lacks a standard procedure that is adequately realistic for clinical tabular data, to our knowledge. We demonstrate that our approach outperforms state-of-the-art baselines in predicting mortality of septic patients (MIMIC-III dataset) and tracking progression of cognitive impairment (ADNI dataset). Our method also consistently recovers the correct synthetic dataset embedding structure across experiments, a feat not achieved by baselines. Our ablation experiments show the pivotal role of our nearest neighbor pairing.

翻译：我们考虑预测患者感兴趣结局发生概率如何随时间推移及观测到更多患者数据而变化的问题。为解决该问题，我们提出一种监督对比学习框架，该框架可为患者时间序列的每个时间步学习嵌入表示。我们的框架使嵌入空间具备以下特性：（1）嵌入空间中邻近点的预测类别概率相似；（2）同一时间序列的相邻时间步映射至嵌入空间中的邻近点；（3）原始特征向量差异显著的时间步映射至嵌入空间的远距离区域。为实现特性（3），我们在原始特征空间中采用最近邻配对机制。该机制同时可作为数据增强的替代方案——数据增强是对比学习的关键要素，但据我们所知，目前尚无充分适用于临床表格数据的标准化增强流程。实验表明，我们的方法在预测脓毒症患者死亡率（MIMIC-III数据集）及追踪认知障碍进展（ADNI数据集）方面均优于当前最先进的基线方法。此外，我们的方法能在所有实验中一致恢复正确的合成数据集嵌入结构，而基线方法无法实现这一目标。消融实验证实了最近邻配对机制的关键作用。