Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
Abstract
Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable tool to gain a deeper understanding of diseases with a long disease trajectory, such as Alzheimer's disease. A key limitation of most disease progression models is that they are specific to a single data type (e.g., continuous data), thereby limiting their applicability to heterogeneous, real-world datasets. To address this limitation, we propose the Mixed Events model, a novel disease progression model that handles both discrete and continuous data types. This model is implemented within the Subtype and Stage Inference (SuStaIn) framework, resulting in Mixed-SuStaIn, enabling subtype and progression modeling. We demonstrate the effectiveness of Mixed-SuStaIn through simulation experiments and real-world data from the Alzheimer's Disease Neuroimaging Initiative, showing that it performs well on mixed datasets. The code is available at: https://github.com/ucl-pond/pySuStaIn.
Cite
@article{arxiv.2602.22018,
title = {Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data},
author = {Sterre de Jonge and Elisabeth J. Vinke and Meike W. Vernooij and Daniel C. Alexander and Alexandra L. Young and Esther E. Bron},
journal= {arXiv preprint arXiv:2602.22018},
year = {2026}
}
Comments
Accepted for publication, 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), April 2026, London, United Kingdom