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This paper introduces GNOVA, a GRU-Neural ODE Variational Autoencoder framework for reconstructing and forecasting Alzheimer's disease cognitive trajectories from routine clinical data without expensive neuroimaging or biomarkers, achieving low error and uncertainty estimation on the ADNI dataset.
This review paper proposes a unified framework for intervention-aware disease trajectory modeling in clinical AI, addressing static prediction failures by incorporating treatment confounder feedback and informative observation patterns.
DT-Transformer is a foundation model trained on 57.1 million structured EHR entries from 1.7 million patients across 11 hospitals in the Mass General Brigham health system, achieving strong discrimination for next-event prediction across 896 disease categories.