Tag
This study evaluates machine learning models for pre-test risk stratification of Chlamydia trachomatis infection using non-invasive patient-reported data and urine biomarkers, demonstrating moderate predictive performance and the complementary value of both data types.
This paper proposes a residual gap-aware transformer that combines a mixed-effects statistical reference with transformer-based residual learning to forecast 24-month CDR-SB change from ADNI clinical and biomarker histories, achieving reduced MSE and improved correlation over baselines.