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.

我们提出了一个深度生成时间序列的方法，利用潜在的时间过程来对复杂疾病轨迹进行建模和全面分析。通过结合生成方法与医学知识，我们展示了学习到的时间潜在过程可用于进一步数据分析和临床假设测试，包括发现相似患者和对疾病进行新的子类型聚类。此外，我们的方法能够进行个性化在线监测和多变量时间序列的预测，包括不确定性量化。我们在建模系统性硬化病过程中展示了我们方法的有效性，展示了我们机器学习模型捕捉复杂疾病轨迹和获取新医学知识的潜力。