混合数据疾病进展建模，突破单一数据类型限制。
Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
- 在SuStaIn框架内融合离散与连续数据建模
- 真实数据验证：在阿兹海默症数据集上表现优异
- 适合多源异构医疗数据的分型与进展分析
疾病进展建模为从短期生物标志物数据中识别长期疾病轨迹提供了稳健框架，对阿尔茨海默病等长周期疾病研究具有重要意义。现有模型多仅适用于单一数据类型（如连续数据），难以应对真实世界中异构数据。为此，我们提出混合事件模型（Mixed Events），将其嵌入子型与阶段推断（SuStaIn）框架，构建出可处理离散与连续数据的混合-子型与阶段推断模型（Mixed-SuStaIn）。通过模拟实验与阿尔茨海默病神经影像计划（ADNI）真实数据验证，结果表明该模型在混合数据集上具备良好性能。代码已开源：https://github.com/ucl-pond/pySuStaIn。
原文摘要 · Abstract (English)
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.
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