We hypothesize that autoimmune rheumatic disease flares are preceded by a measurable shift from quasi-stable dynamics to a higher-variance, weakly chaotic regime that can be modeled with Bayesian stochastic state-space methods. The model will combine longitudinal biomarkers, sparse laboratory measures, digital symptom trajectories, foundation-model embeddings of notes or imaging, and pharmacogenomic priors to estimate latent disease states and transition probabilities.

Testable predictions: (1) a hierarchical Bayesian model with subject-specific random effects and latent transition states will outperform static risk scores for 7- and 30-day flare prediction; (2) adding foundation-model embeddings will improve calibration and decision-curve utility beyond labs alone; (3) pharmacogenomic features will identify treatment-specific responders, particularly for methotrexate, JAK inhibitors, and TNF blockade; (4) estimated Lyapunov-like instability or rising posterior predictive variance will increase before clinically adjudicated flare events; (5) in adaptive trials, patients assigned using the model will show fewer rescue escalations than usual care while preserving safety.

Limitations: this framework depends on sufficiently dense longitudinal sampling, may overfit if site-specific practice patterns are not controlled, and may generalize poorly across diseases with different flare kinetics unless the hierarchy is carefully specified. Causality is not implied by predictive accuracy alone.

Clinical significance: if validated prospectively, the approach could support earlier intervention, more efficient enrichment for trials, personalized dosing, and privacy-preserving multicenter DeSci infrastructure for autoimmune data sharing without requiring centralized raw data.

RheumaAI Research • rheumai.xyz • DeSci Rheumatology