The Coupled Stochastic Dynamical System is introduced, a forward generative model that jointly simulates digital engagement, weara-ble physiology, and psychiatric burden as a family of coupled stochastic processes and position generative behaviour-al models as falsifiable and interpretable frameworks for studying youth mental-health trajectories and for developing future personalised interventions.
Human health is a single underlying state that no measurement observes directly: diagnoses, blood tests, molecular profiles and images each capture one facet at separate times. Inferring health from such evidence requires a representation that integrates every modality and is carried forward and revised as observations...
Z. Wang, O. Zhang, J. Wang et al.· medRxiv· 1 citation
The Neural ODE-LMM is proposed, which embeds a Neural Ordinary Differential Equation (Neural ODE) within the linear mixed-effects framework: a learned vector field encodes covariate trajectories into a continuous-time latent state that drives both the fixed- and random-effect design, while preserving the standard LMM o...
Zhe Li, Q. Clairon, C. Samieri et al.· 0 citations
Life Operators is proposed: task-bounded mappings that define three scientific roles: Perception operators infer task-relevant biological states from multimodal observations, Evolution operators propagate these states under natural or intervention-conditioned dynamics, and Generation operators map them to measurable si...
TIDE, a Treatment-Informed Dynamics Encoder, a diagonal continuous-time state space uses the true inter-observation interval as its integration step and lets the exogenous intervention channel modulate the decay eigenvalues, so an intervention alters the rate law rather than the level.
Juslin Franklin, Gopalakrishna Kanathur· Journal of Intelligent Decis...· 0 citations
Human health is a continuous, dynamic trajectory shaped by the cumulative interplay of biological processes, clinical events, behaviors and environmental exposures across the life course. Unifying the full breadth of lifelong health information, including longitudinal records, genetic variation, molecular profiles and...
Jin Jiang, Sean Yates, Jasper Chong et al.· 0 citations
Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients'longitudinal trajectory to improve the prediction of survival outcomes. The classical parametric joint models, however, rely on fixed parametric assumptions, making them susceptible to bias under model misspecification...
B. Halder, J. Thompson· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.