  This work proposes a novel framework for analyzing disease progression using
time-aware neural ordinary differential equations (NODE). We introduce a
"time-aware head" in a framework trained through self-supervised learning (SSL)
to leverage temporal information in latent space for data augmentation. This
approach effectively integrates NODEs with SSL, offering significant
performance improvements compared to traditional methods that lack explicit
temporal integration. We demonstrate the effectiveness of our strategy for
diabetic retinopathy progression prediction using the OPHDIAT database.
Compared to the baseline, all NODE architectures achieve statistically
significant improvements in area under the ROC curve (AUC) and Kappa metrics,
highlighting the efficacy of pre-training with SSL-inspired approaches.
Additionally, our framework promotes stable training for NODEs, a commonly
encountered challenge in time-aware modeling.
