Skalibrowana czasowa mapa progresji chorób

Background Understanding the evolution of human illness requires capturing the temporal directionality of disease progression, yet existing biomedical reference maps largely describe cross-sectional states or static comorbidity. We introduce a directed, probability-ranked map (i.e., a knowledge-base) of clinical progression derived from population-scale longitudinal electronic health records. Methods The knowledge-base was constructed from de-identified EHRs of 295,678 individuals across the Mass General Brigham system, yielding 435,240 phenotype-pair-duration associations via temporal Spearman correlation. To distinguish biological progression from administrative artefact at scale, we distilled a locally deployed MedGemma labeling function into two complementary classifiers: a RF capturing local episodic signal and a GNN aggregating global network topology via message passing. Their outputs were combined as an unweighted late-fusion average. Classifier confidence was systematically evaluated against pairwise genome-wide genetic correlation estimates from the UK Biobank as an independent biological reference standard. Results Both classifiers achieved comparable distillation fidelity on the 200-row development set (RF AUROC 0.772; GNN AUROC 0.769). Genetic support was concentrated in the highest confidence deciles, with both models achieving highly significant top-decile enrichment for validated genetic pleiotropy (RF: 1.36-fold, p Conclusions By transitioning from static comorbidity networks to a confidence-ranked landscape of temporal trajectories, the map provides a biologically calibrated coordinate system for prioritising mechanistic, translational, and clinical investigation of disease progression.