@inproceedings{
trottet2023generative,
title={Generative Time Series Models with Interpretable Latent Processes for Complex Disease Trajectories},
author={C{\'e}cile Trottet and Manuel Sch{\"u}rch and Amina Mollaysa and Ahmed Allam and Michael Krauthammer},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=tiqs7trqcC}
}
We propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex 
disease trajectories and demonstrate its effectiveness in modeling systemic sclerosis. 
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.
We show that the learned temporal latent processes can be utilized for further data analysis, 
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.
Generative Time Series Models with Interpretable Latent Processes for Complex Disease Trajectories
Cécile Trottet Manuel Schürch Amina Mollaysa Ahmed Allam Michael Krauthammer