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.

翻译：本文提出了一种新颖的框架，用于分析疾病进展，该框架采用时间感知神经常微分方程（NODE）。我们在一个通过自监督学习（SSL）训练的框架中引入“时间感知头部”，以利用潜在空间中的时间信息进行数据增强。该方法有效整合了NODE与SSL，相较于缺乏显式时间整合的传统方法，显著提升了性能。我们利用OPHDIAT数据库验证了该策略在糖尿病视网膜病变进展预测中的有效性。与基线相比，所有NODE架构在ROC曲线下面积（AUC）和Kappa指标上均取得了统计显著的提升，突显了采用SSL启发方法进行预训练的有效性。此外，我们的框架促进了NODE的稳定训练——这是时间感知建模中常见的挑战。