A calibrated temporal reference map of disease progression

Background. Understanding the evolution of human illness requires capturing the temporal direc- tionality 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 systemati- cally 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 confi- dence deciles, with both models achieving highly significant top-decile enrichment for validated genetic pleiotropy (RF: 1.36-fold, p < 0.001; GNN: 1.32-old, p < 0.001), demonstrating that classifier confi- dence aligned with independent genomic support. The framework additionally identified two comple- mentary classes of progression: acquired mechanical cascades with high classifier confidence but null genomic overlap (exemplified by musculoskeletal pain progressing to cardiac dysrhythmias beyond 90 days, Pavg = 0.984, rg = 0.046, prg = 0.539), and topological bridges structurally enforced by network architecture despite sparse local co-occurrence (exemplified by acute myocardial infarction to epilepsy within 0-14 days, PGNN = 0.930 versus PRF = 0.332). 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.