发表机构
School of Computer Science, University of Sheffield（谢菲尔德大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对数字移动性结局跨疾病纵向建模的缺口，提出可解释框架DeMMO，其通过自动跨疾病跨结局关系学习机制实现选择性信息共享，在Mobilise-D数据集上优于9种基线，性能显著提升。
AI 中文摘要
源自可穿戴传感器的数字移动性结局（DMOs）可表征日常生活中的移动能力，为监测疾病进展提供了极具前景的手段。然而，大多数DMO研究仅针对单一疾病在单次访视中开展，并未建模多元DMO与多种临床结局的关系如何跨疾病联合演变。技术层面，现有的时序多任务框架可对单一疾病内的进展进行建模，但无法跨疾病联合建模多个预测结局，尤其当疾病队列不共享参与者时更是如此。为解决这些缺口，我们提出DeMMO，这是一个适用于纵向、多疾病及多结局学习的可解释框架。DeMMO通过纵向DMO系数矩阵表征每个疾病-结局目标，并将时序正则化与稳定且访视特定的特征选择相结合。其核心技术贡献是一种自动跨疾病与跨结局关系学习机制，可直接从这些纵向映射中学习符号关系，从而在无需配对参与者的情况下实现选择性信息共享。我们在最新发布的大规模多中心Mobilise-D数据集上对DeMMO进行评估，该数据集为研究多种移动受限病症的5次访视中24项协调一致的真实世界DMO提供了新机遇。与9种强大的线性、时序及深度回归基线相比，DeMMO取得了最佳的整体及结局特定预测性能，且相较于最强基线存在显著提升。稳定性选择进一步识别出可靠的纵向DMO模式，以供后续临床验证与疾病监测。实现代码及实验结果可在该httpsURL获取。
英文摘要
Digital mobility outcomes (DMOs) derived from wearable sensors characterise mobility in daily life and offer a promising means of monitoring disease progression. However, existing DMO studies have typically focused on either a single disease or a single visit. To the best of our knowledge, we are the first to define and study the practical problem of cross-disease longitudinal DMO modelling. We argue that this problem should satisfy at least two requirements. First, the temporal progression of DMOs should be modelled within each disease, as mobility-limiting diseases evolve over time. Second, multiple mobility-limiting diseases should be modelled jointly, as different diseases affect different aspects of human mobility. To address this problem, we propose DeMMO, an interpretable framework for longitudinal, multi-disease, and multi-outcome learning. Its central technical contribution is an interpretable cross-disease and cross-outcome relation-learning mechanism that infers signed relations directly from learned longitudinal DMO-outcome mappings, thereby enabling selective information sharing across cohorts without requiring paired participants. We evaluate DeMMO on the recently released, large-scale, multicentre Mobilise-D dataset, which presents a challenging modelling setting involving longitudinal observations, multiple clinical outcomes, and four participant-disjoint cohorts representing distinct mobility-limiting diseases. Compared with eight strong structural longitudinal and deep-regression baselines, DeMMO achieves the best overall predictive performance and outperforms the baselines for most individual outcomes. Stability selection further identifies reliable longitudinal DMO patterns that can inform subsequent clinical validation and disease monitoring. The implementation code is available at https://github.com/menghui-zhou/DeMMO.
Comments24 pages, 5 figures, and 8 tables. Implementation code and experimental results are available at https://github.com/menghui-zhou/DeMMO
