In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. We aim to find meaningful temporal latent representations of an underlying generative process that explain the observed 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 concepts. By combining the generative approach with medical knowledge, we leverage the ability to discover novel aspects of the disease while integrating medical concepts into the model. 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 the disease into new sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series including uncertainty quantification. We demonstrate the effectiveness of our approach in modeling systemic sclerosis, showcasing the potential of our machine learning model to capture complex disease trajectories and acquire new medical knowledge.

翻译：本文提出一种基于潜在时间过程的深度生成时间序列方法，用于对复杂疾病轨迹进行建模与整体分析。我们旨在发现底层生成过程中有意义的潜在时间表征，以可解释且全面的方式解释观察到的疾病轨迹。为提升这些潜在时间过程的可解释性，我们开发了一种半监督方法，利用既定医学概念对潜在空间进行解耦。通过将生成方法与医学知识相结合，我们能够在整合医学概念的同时，挖掘疾病的新特征。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与临床假设检验，包括寻找相似患者以及将疾病聚类为新的亚型。此外，我们的方法可实现个性化在线监测与多元时间序列预测，并包含不确定性量化。我们通过在系统性硬化症建模中验证该方法的有效性，展示了机器学习模型捕获复杂疾病轨迹并获取新医学知识的潜力。