English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we propose a deep generative time series approach using latenttemporal processes for modeling and holistically analyzing complex diseasetrajectories. We aim to find meaningful temporal latent representations of anunderlying generative process that explain the observed disease trajectories inan interpretable and comprehensive way. To enhance the interpretability ofthese latent temporal processes, we develop a semi-supervised approach fordisentangling the latent space using established medical concepts. By combiningthe generative approach with medical knowledge, we leverage the ability todiscover novel aspects of the disease while integrating medical concepts intothe model. We show that the learned temporal latent processes can be utilizedfor further data analysis and clinical hypothesis testing, including findingsimilar patients and clustering the disease into new sub-types. Moreover, ourmethod enables personalized online monitoring and prediction of multivariatetime series including uncertainty quantification. We demonstrate theeffectiveness of our approach in modeling systemic sclerosis, showcasing thepotential of our machine learning model to capture complex disease trajectoriesand acquire new medical knowledge.

Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes