This research aims to reconstruct and forecast Alzheimer's disease trajectories using routine data in resource-constrained settings. It proposes a unified framework for bidirectional prediction of cognitive scores from irregular visits, enabling interpolation and extrapolation, and providing calibrated uncertainty estimates.
This study leverages nationwide longitudinal EHR data from the All of Us Research Program to predict chronic rhinosinusitis (CRS) diagnosis using two years of pre-diagnostic history. It implements a hybrid feature-selection pipeline to address data sparsity and dimensionality, aiming to overcome limitations of single-institutional cohorts and improve population-level generalizability.
This research introduces a method for detecting transient liquidity erosion ("crumbling quotes") in electronic limit order books, differentiating between mechanical liquidity withdrawal and informational repricing. Using an ABIDES multi-agent simulator for ground truth, a neural model is developed that significantly outperforms rule-based baselines in identifying crumbling events across diverse market conditions.
This study develops a predictive framework to model the Heavy Metal Pollution Index (HPI) in groundwater, integrating response transformations with nested cross-validated ensemble machine learning. It aims to overcome challenges posed by statistical complexity and spatial heterogeneity of contaminants that affect conventional prediction methods.