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Research questionHow can wearable-derived mobility outcomes be modelled longitudinally across diseases without paired participants?Mobility-limiting diseases progress differently and affect different aspects of daily movement, while cohorts may contain different participants for each disease. The modelling problem therefore requires capturing within-disease temporal change alongside relationships across diseases and outcomes.
AI
Health
Machine Learning
Research Paper
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.DeMMO: Longitudinal and Cross-Disease Modelling of Digital Mobility Outcomes via Multi-Task LearningThe setting concerns wearable-sensor-derived digital mobility outcomes and longitudinal clinical-outcome prediction across four participant-disjoint disease cohorts in the multicentre Mobilise-D dataset. Evidence covers comparisons with eight structural longitudinal and deep-regression baselines, predictive performance across outcomes, and stability-selected longitudinal patterns; it does not establish subsequent clinical validation or deployment impact.research paper · Sep 3, 2026
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