Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F24%3A10500427" target="_blank" >RIV/00216208:11110/24:10500427 - isvavai.cz</a>
Result on the web
<a href="https://proceedings.mlr.press/v252/trottet24a.html" target="_blank" >https://proceedings.mlr.press/v252/trottet24a.html</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis
Original language description
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.
Czech name
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Czech description
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Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 9th Machine Learning for Healthcare Conference
ISBN
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ISSN
2640-3498
e-ISSN
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Number of pages
42
Pages from-to
1-42
Publisher name
ML Research Press
Place of publication
San Diego
Event location
Toronto
Event date
Aug 16, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001483857800033

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