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.

我们提出了一种监督对比学习框架，通过学习每个时间步长的患者时间序列的嵌入表示来预测患者在随着观察到更多患者数据的过程中感兴趣结果的可能性变化。我们的方法在预测败血症患者的死亡率（MIMIC-III 数据集）和追踪认知障碍的进展（ADNI 数据集）方面优于现有的基准方法，并且在实验中始终能够正确恢复合成数据集的嵌入结构。