# Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data

arXiv: https://arxiv.org/abs/2602.22018 · HTML: https://mlsanity.com/paper/2602.22018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 1 of 1)

- [Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling](https://mlsanity.com/paper/1808.03604/) (2018)

## Built on by (most cited first, 1 of 1)

- [Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum](https://mlsanity.com/paper/2608.19436/) (2026)

## Abstract (arXiv)

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.