Innovative Application of Knowledge Graph-Driven Causal Inference in Digital Twin of Chronic Disease Progression

A patient-centric digital-twin architecture that fuses ontology-grounded knowledge graphs with structural causal inference is presented to simulate the five-year evolution of type 2 diabetes mellitus and cardio-renal comorbidities. A harmonised health-information-exchange corpus comprising 12 318 adults, 22.7 million encounter rows and 7.4 million laboratory records (2010 2024) was mapped to a 168 402-node, 1 217 965-edge graph aligned to SNOMED-CT. Counterfactual trajectories under 17 therapeutic bundles were generated by a differentiable do-calculus engine nested inside a temporal graph transformer, producing 1 000 Monte-Carlo roll-outs per patient. External validation on an independent 2 975-subject cohort yielded a dynamic concordance index of 0.842, an integrated Brier score of 0.091 and a calibration-in-the-large of 0.013, surpassing recurrent neural and mechanistic baselines by 18.5 % and 11.2 % respectively. Sensitivity analyses confirmed robustness to 24 % MCAR missingness and 15 % hidden-confounding bias. The findings demonstrate that knowledge-graph-driven causal twins deliver granular, well-calibrated forecasts and quantitatively rank preventive strategies, paving the way for learning-health-system deployment in chronic-disease management.