A computer-implemented
system for personalized health optimization constructs a confidence-weighted personal
health knowledge graph (PHKG) from heterogeneous data, including wearable sensors, medical devices, lab results, medication logs, and conversational inputs. A multi-stage causal-
inference stack identifies modifiable drivers of outcomes using layered methods (e.g., MI, GAM, Neural Granger, DAG-GNN), and simulates candidate interventions. A recommendation engine ranks lifestyle or pharmacologic actions using a benefit-to-friction
score, selecting a personalized intervention aligned with user readiness and
clinical safety constraints. Interventions may include a minimum effective
dose (MED), optimal level, adaptive low-
dose, or behavioral challenge. Optional modules include
reinforcement learning for timing
adaptation and privacy-preserving on-device
inference. The
system operates across domains including metabolic, cardiovascular, renal, sleep, stress, and medication response, enabling cross-condition
synergy evaluation. The architecture is modular, supports runtime plug-in targets, and adapts in real time with or without continuous clinical oversight, depending on deployment.