Temporal Supervised Contrastive Learning for Modeling Patient Risk
  Progression
Shahriar Noroozizadeh Jeremy C. Weiss George H. Chen
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.