联合进展建模（JPM）：一种用于混合病理进展的概率框架
Joint Progression Modeling (JPM): A Probabilistic Framework for Mixed-Pathology Progression
AI总结：
JPM通过概率框架处理混合病理进展，提升排序准确性21%，并与文献结果一致。
摘要：
基于事件的模型（EBMs）从横断面数据推断疾病进展，标准EBMs假设每个个体有单一的潜在疾病。相比之下，神经退行性疾病中混合病理是常见的。我们引入了联合进展模型（JPM），一种概率框架，将单疾病轨迹视为部分排名，并构建了联合进展的先验。我们研究了几种JPM变体（成对、布拉德利-泰利、普莱特-卢斯和马洛斯），并分析了三个性质：（i）校准——是否较低的模型能量预测更小的距离到真实顺序；（ii）分离——采样排名与随机排列区分的程度；（iii）锐度——采样聚合排名的稳定性。所有变体都经过校准，所有变体都实现了接近完美的分离；锐度因变体而异，且可通过输入部分排名的简单特征（排名数量和长度、冲突和重叠）良好预测。在合成实验中，JPM在强EBM基线（SA-EBM）上将排序准确性提高了约21%。最后，使用NACC，我们发现JPM的马洛斯变体和基线模型（SA-EBM）的结果更符合关于阿尔茨海默病和血管性痴呆混合病理可能进展的文献。
英文摘要：
Event-based models (EBMs) infer disease progression from cross-sectional data, and standard EBMs assume a single underlying disease per individual. In contrast, mixed pathologies are common in neurodegeneration. We introduce the Joint Progression Model (JPM), a probabilistic framework that treats single-disease trajectories as partial rankings and builds a prior over joint progressions. We study several JPM variants (Pairwise, Bradley-Terry, Plackett-Luce, and Mallows) and analyze three properties: (i) calibration -- whether lower model energy predicts smaller distance to the ground truth ordering; (ii) separation -- the degree to which sampled rankings are distinguishable from random permutations; and (iii) sharpness -- the stability of sampled aggregate rankings. All variants are calibrated, and all achieve near-perfect separation; sharpness varies by variant and is well-predicted by simple features of the input partial rankings (number and length of rankings, conflict, and overlap). In synthetic experiments, JPM improves ordering accuracy by roughly 21 percent over a strong EBM baseline (SA-EBM) that treats the joint disease as a single condition. Finally, using NACC, we find that the Mallows variant of JPM and the baseline model (SA-EBM) have results that are more consistent with prior literature on the possible disease progression of the mixed pathology of AD and VaD.
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