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[2602.22018] Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
Summary
This article presents a novel disease progression model, Mixed-SuStaIn, which integrates both discrete and continuous data types to improve understanding of diseases like Alzheimer's.
Why It Matters
The ability to model disease progression using mixed data types enhances research capabilities in complex diseases, potentially leading to better patient outcomes and tailored treatment strategies. This advancement addresses a significant limitation in existing models that often focus on a single data type.
Key Takeaways
- Mixed-SuStaIn model effectively combines discrete and continuous data for disease progression modeling.
- The model addresses limitations of traditional models that only handle single data types.
- Demonstrated effectiveness through simulation and real-world Alzheimer's data.
- Code availability promotes further research and application in the field.
- Potential to improve understanding and treatment of diseases with long trajectories.
Computer Science > Machine Learning arXiv:2602.22018 (cs) [Submitted on 25 Feb 2026] Title:Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data Authors:Sterre de Jonge (1), Elisabeth J. Vinke (1,2), Meike W. Vernooij (1,2), Daniel C. Alexander (3), Alexandra L. Young (3), Esther E. Bron (1) ((1) Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands, (2) Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands, (3) Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom) View a PDF of the paper titled Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data, by Sterre de Jonge (1) and 18 other authors View PDF HTML (experimental) 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, re...
