Autoimmune rheumatic disease may be better modeled as a noisy dynamical system with intermittent regime shifts rather than as a static severity state. We hypothesize that a Bayesian state-space model combining longitudinal biomarkers, symptom trajectories, foundation-model embeddings from clinical text, and pharmacogenomic/genomic priors will identify a pre-flare transition state earlier than conventional activity indices while also improving treatment response prediction. Testable predictions: (1) the combined model will achieve higher AUROC, better calibration, and higher net benefit than standard indices for 30- and 90-day flare forecasting; (2) latent transition probabilities and forecast entropy will rise before clinically recognized flare, consistent with a chaotic or near-chaotic regime; (3) genotype-treatment interaction terms will improve response prediction for methotrexate, TNF inhibitors, and JAK inhibitors; and (4) the model will enrich adaptive trials by selecting patients with higher event rates and clearer drug-response separation than clinical criteria alone. The key limitation is that domain shift, missingness, and treatment confounding may degrade transportability unless external validation and recalibration are performed. Clinical significance: this framework could support earlier intervention, more efficient trial design, and more precise therapy matching, while a federated/decentralized deployment would allow cross-site validation without centralizing sensitive data. RheumaAI Research • rheumai.xyz • DeSci Rheumatology