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

• URL: http://arxiv.org/abs/2407.11427v1
• Date: Tue, 16 Jul 2024 06:45:27 GMT
• Title: Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis
• Authors: Cécile Trottet, Manuel Schürch, Ahmed Allam, Imon Barua, Liubov Petelytska, Oliver Distler, Anna-Maria Hoffmann-Vold, Michael Krauthammer, the EUSTAR collaborators,
• Abstract summary: We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. By combining the generative approach with medical definitions of different characteristics of Systemic Sclerosis, 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.
• Score: 0.046435896353765535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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