arXiv:2604. 07557v2 Announce Type: replace Abstract: Small longitudinal cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling because enrollment is slow and the data are too sparse to train reliable models.
arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.
arXiv:2608. 00073v1 Announce Type: cross Abstract: Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging.
arXiv:2607. 11656v1 Announce Type: cross Abstract: Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data.
arXiv:2608. 08244v1 Announce Type: new Abstract: General wearable foundation models are pretrained across broad sensor streams and populations, but are not designed around women's-health tasks.
arXiv:2607. 15721v1 Announce Type: new Abstract: Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease frequently co-occur and share metabolic, vascular, demographic, and behavioral determinants.
arXiv:2607. 26752v1 Announce Type: new Abstract: Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting downstream tasks from imaging-based diagnosis to digital-twin treatment planning.
arXiv:2608. 09460v1 Announce Type: new Abstract: Cardiac digital twin research is moving from subject-specific anatomical replicas toward virtual cohorts that represent clinically relevant population subgroups.
arXiv:2606. 02228v1 Announce Type: cross Abstract: Predicting whether an individual with Alzheimer's disease will experience mild or severe disease progression is essential for personalized treatment.
arXiv:2606. 15637v1 Announce Type: new Abstract: A digital twin (DT) of a patient-specific heart offers significant potential in personalized medicine.
arXiv:2606. 07488v1 Announce Type: new Abstract: Personalized virtual heart simulations face challenges in model personalization and computational cost.
arXiv:2607. 28567v1 Announce Type: cross Abstract: Longitudinal causal studies often record histories as irregular functional fragments: laboratory values, physiologic signals, sensor streams, and image-derived summaries measured at unequal and informative times.
arXiv:2510. 19893v2 Announce Type: replace Abstract: Medical AI systems demonstrated impressive diagnostic performance, yet they routinely show uneven accuracy across demographic groups, disadvantaging underrepresented populations.