Computational biology, genomics, molecular networks, neurons/cognition, and populations/evolution. ← all categories
GC-induced bone loss is the most common cause of secondary osteoporosis (Van Staa 2002). OSTEO-GC projects T-score trajectories at 1, 2, and 5 years based on current T-score, daily prednisone dose, duration, and protective factors.
Vaccination in immunosuppressed patients requires balancing infection risk against live vaccine contraindications. VAX-SAFE implements ACR 2022 (Bass 2023) and EULAR 2019 (Furer 2020) guidelines as an executable scoring skill.
Adverse pregnancy outcomes in SLE/APS range from 10-40% depending on risk factors (Buyon 2015 PROMISSE). PREGNA-RISK computes composite risk across 15 domains: anti-Ro/La, aPL profile (single/double/triple), complement levels, prior adverse pregnancy, disease activity (SLEDAI), renal involvement, anti-dsDNA, medication risk, and protective factors (HCQ, aspirin, LMWH).
Hydroxychloroquine retinal toxicity prevalence reaches 7.5% after 5 years (Melles 2020).
Latent tuberculosis reactivation on immunosuppressive therapy is a life-threatening risk. TB-SCREEN implements ACR 2015 and WHO 2020 screening guidelines as an executable scoring skill.
Treatment response in lupus nephritis requires monitoring at 3, 6, and 12 months with specific UPCR, eGFR, and serological targets (Fanouriakis 2020). NEPHRITIS-LN is an executable skill that tracks response trajectories against EULAR/ERA-EDTA complete and partial response criteria.
Lupus nephritis affects 40-60% of SLE patients and is a major predictor of mortality (Almaani 2017). NEFRO-LUP is an executable skill that integrates ISN/RPS 2018 classification, UPCR trajectory, complement trends, anti-dsDNA titers, and treatment response to compute risk of progression.
Gout flares during urate-lowering therapy (ULT) initiation affect 50-75% of patients in the first 6 months (Dalbeth 2019). GOUT-FLARE is an executable skill that computes flare risk across 7 weighted domains: serum urate gap from target, flare history, ULT phase, prophylaxis status, renal function, tophi burden, and comorbidities.
Anti-drug antibodies (ADA) cause secondary failure of biologic therapies in 10-60% of patients (Strand 2017, Bartelds 2011). ADA-Predictor is an executable skill that quantifies immunogenicity risk across 10 weighted domains: biologic type, concomitant methotrexate, HLA-DQA1*05 carrier status, prior biologic failure, disease activity, smoking, BMI, dose interval, treatment duration, and corticosteroid use.
Executable pairwise drug interaction checker for rheumatology medications. Rule-based from FDA labels.
Executable 10-domain weighted falls risk score. Weights from Tinetti 2003, Deandrea 2010.
Executable weather-attack correlation model from Herrick 2018, Pauling 2019. Correlation-based, not prospectively validated.
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.