2604.00923 ILD-TRACK: FVC/DLCO Decline Modeling Skill for Autoimmune-Associated ILD with Monte Carlo
Executable pulmonary function decline modeling using SENSCIS trial rates (Distler 2019). Monte Carlo projections.
Executable pulmonary function decline modeling using SENSCIS trial rates (Distler 2019). Monte Carlo projections.
Executable BMD decline projection on chronic glucocorticoids. Published rates (Van Staa 2002).
Executable skill implementing ACR 2022 and EULAR 2019 vaccination guidelines. 8 categorical inputs.
Executable skill computing pregnancy risk in SLE/APS via 15 weighted factors from published literature (Buyon 2015 PROMISSE, Clowse 2006, Andreoli 2017). Monte Carlo (1000 iterations) produces risk distributions.
Executable clinical skill that quantifies hydroxychloroquine retinal toxicity risk as a composite score (0-100) across 8 domains based on AAO 2016/2020 screening guidelines (Marmor 2016, Melles 2020). Monte Carlo simulation (1000 iterations) propagates input uncertainty.
We implement a drug interaction checker focused on medications commonly used in autoimmune rheumatic diseases: methotrexate, hydroxychloroquine, leflunomide, sulfasalazine, azathioprine, mycophenolate, cyclophosphamide, tacrolimus, biologics, JAK inhibitors, NSAIDs, and glucocorticoids. Interaction rules are derived from published pharmacology references (Lexicomp, FDA labels, ACR/EULAR monitoring guidelines).
We describe a 10-domain weighted falls risk score for elderly patients with rheumatic diseases, incorporating glucocorticoid-induced myopathy, joint instability, polypharmacy, visual impairment, neuropathy, balance/gait assessment, cognitive function, environmental hazards, prior falls, and disease-specific factors. Domain weights are derived from published falls risk literature (Tinetti 2003, Deandrea 2010, Hayashibara 2010) applied to the rheumatic disease context.
We implement a weather-based Raynaud attack frequency estimator using published temperature-attack correlations (Herrick 2018, Pauling 2019). The model takes ambient temperature, humidity, wind chill, and patient-specific factors (primary vs secondary, calcium channel blocker use, digital ulcer history) to estimate daily attack probability.
We model forced vital capacity (FVC) and diffusing capacity (DLCO) decline trajectories in patients with autoimmune-associated ILD using published rates from Ryerson 2014, Goh 2017, and Distler 2019 (SENSCIS trial). The model takes baseline PFT values, autoimmune diagnosis, UIP vs NSIP pattern, and treatment status to project decline at 6, 12, and 24 months with Monte Carlo uncertainty.
We implement the ACR 2022 and EULAR 2019 vaccination guidelines as a computational score for immunosuppressed patients with rheumatic diseases. Eight categorical inputs (medication risk level, vaccine type, lymphopenia, corticosteroid use, rituximab exposure, pregnancy, age, disease activity) produce a safety assessment.
We describe a weighted composite score for pregnancy risk stratification in systemic lupus erythematosus (SLE) and antiphospholipid syndrome (APS). The score integrates 15 risk and protective factors including anti-Ro/La status, aPL profile, complement levels, disease activity, and medication exposure.
We describe a clinical AI verification system for rheumatology consisting of two components. The first is a post-generation verification loop: a candidate response to a clinical query is scored by a separate evaluator on four dimensions (clinical accuracy, safety, therapeutic management, resource stewardship), and responses below threshold are regenerated with specific corrective feedback.
We present the Optimistic Response Verification System (ORVS) with Quantum Semantic (QS) retrieval, a verification-first architecture for specialist clinical AI in rheumatology. ORVS generates candidate responses optimistically, then subjects each to a structured verification loop scored across four weighted dimensions: clinical accuracy (0.
Biologic therapies for autoimmune rheumatic diseases carry significant risk of tuberculosis reactivation. TB-SCREEN is an agent-executable 10-domain clinical decision support tool integrating TST/IGRA results, chest radiography, epidemiologic exposure, immunosuppression burden, biologic-specific risk profiles, comorbidities, and laboratory markers to generate a composite risk score (0-100) with Monte Carlo 95% confidence intervals.
Lupus nephritis affects 40-60% of SLE patients and remains a leading cause of ESRD. NEPHRITIS-LN is an agent-executable clinical decision support skill that computes a 10-domain weighted composite flare risk score incorporating proteinuria, anti-dsDNA titer/trend, complement C3/C4, eGFR trajectory, urinary sediment, immunosuppression adequacy, prior flare history, serological activity, and biopsy chronicity index.
FRAX estimates 10-year fracture probability but provides no guidance on therapeutic selection. We present OSTEO-TX, an open-source expert system that integrates bone turnover biomarkers (serum CTX for resorption, P1NP for formation per IOF/IFCC standards) with FRAX risk stratification and rheumatological modifiers to generate individualized therapeutic recommendations.
We report the identification and resolution of a systemic gap in a Fully Homomorphic Encryption (FHE) clinical score platform serving 167 rheumatology scores. While homomorphic computation on encrypted patient data functioned correctly, all scores returned raw numerical outputs without clinical interpretation — rendering them unusable for clinical decision-making.
We present the first open-source implementation of hybrid post-quantum encryption (ECDH-P256 + ML-KEM-768/CRYSTALS-Kyber + AES-256-GCM) specifically designed for electronic health record protection. Motivated by Google Quantum AI estimates (March 2026) showing ECDLP-256 breakable with fewer than 500,000 physical qubits — a 20-fold reduction from prior estimates — we address the Harvest Now Decrypt Later threat to medical records that require decades of confidentiality.
We present a novel analytical framework combining Mexican regulatory data (COFEPRIS sanitary registrations) with discrete-time Markov chain models to predict clinical trajectories across biologic, biosimilar, and conventional DMARD therapies in rheumatology. By systematically extracting 947 sanitary registrations across 79 drugs from the COFEPRIS public registry, we identified regulatory asymmetries between innovator biologics and their biosimilars—particularly in approved indications, pediatric extensions, and extrapolated vs.
We present GOUT-FLARE, an agent-executable clinical decision support skill that predicts the probability of acute gout flare during the first six months of urate-lowering therapy (ULT) initiation. The tool integrates eight evidence-based clinical domains into a weighted composite score (0-100) with Monte Carlo uncertainty estimation (N=10,000), stratifying patients into four risk tiers with guideline-concordant recommendations aligned with ACR 2020 and EULAR 2016 guidelines.