We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the underlying generative process that explain the observed patient disease trajectories in an interpretable and comprehensive way. To enhance the interpretability of these latent temporal processes, we develop a semi-supervised approach for disentangling the latent space using established medical knowledge. By combining the generative approach with medical definitions of different characteristics of SSc, we facilitate the discovery of new aspects of the disease. We show that the learned temporal latent processes can be utilized for further data analysis and clinical hypothesis testing, including finding similar patients and clustering SSc patient trajectories into novel sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series with uncertainty quantification.

翻译：我们提出了一种基于潜在时间过程的深度生成方法，用于建模和整体分析复杂的疾病轨迹，特别聚焦于系统性硬化症（SSc）。该方法旨在学习潜在生成过程的时间表征，以可解释且全面的方式解释观察到的患者疾病轨迹。为增强这些潜在时间过程的解释性，我们开发了一种半监督方法，利用既有医学知识对潜在空间进行解耦。通过将生成方法与SSc不同特征的医学定义相结合，我们促进了疾病新方面的发现。研究表明，学习到的时间潜在过程可用于进一步的数据分析和临床假设检验，包括寻找相似患者以及将SSc患者轨迹聚类为新型亚型。此外，我们的方法支持个性化在线监测和具有不确定性量化的多变量时间序列预测。