KuraPath
know your health
Synthetic Health
Data Engine
Generate clinically realistic synthetic health data across 44 biomarkers, 9 panels, and 74 languages. Differential privacy, adversarial validation, and population cohort generation. Zero privacy risk. Audit-ready.
How It Works
Four-stage pipeline from configuration to validated synthetic output.
Configure
Define demographics, age ranges, gender distribution, condition profiles, and select from 9 panels covering 44 biomarkers.
Generate
Parametric engine samples from age/gender-stratified population distributions (RCPA, AIHW, NHS reference data). Seeded PRNG ensures reproducibility.
Privatise
Laplace differential privacy noise (ε=5.0) is applied to all underlying distributions, preventing memorisation of any real population data.
Validate
9 adversarial vectors test every batch: boundary violations, physiological impossibility, cross-biomarker consistency, and safety scoring.
Platform Capabilities
Enterprise-grade synthetic data generation built for pharma and clinical research.
44 Biomarkers × 9 Panels
Full Blood Count, Lipid Profile, Liver Function, Renal, Thyroid, Iron Studies, Metabolic/Diabetes, Vitals/Anthropometrics, and Bone & Mineral panels with clinically accurate reference ranges.
74 Languages
12 Tier-1 languages with full medical term translations (EN, ZH, AR, VI, HI, EL, IT, KO, TL, PA, TA + Cantonese) and 60 Tier-2 languages with transliterated labels.
Population Cohorts
Generate up to 10,000 synthetic profiles across named populations. Configure age ranges, gender ratios, conditions, and panels per cohort.
Seeded Reproducibility
Deterministic generation via Mulberry32 PRNG. The same seed and parameters always produce identical output, making every batch audit-ready.
Adversarial Testing
9 attack vectors including prompt injection, Unicode homoglyphs, extreme values, diagnostic coercion, and mixed real/fake detection. Safety-scored per batch.
Branded CSV Export
Server-side CSV generation with KuraPath header, metadata, UTF-8 BOM for Excel compatibility, and flat-table format for direct statistical import.
Use Case Studies
How pharmaceutical and clinical research organisations use synthetic health data.
Note:The following case studies feature fictional companies created for illustrative purposes. They demonstrate representative use cases and projected outcomes based on the platform's capabilities. No real organisations or patient data are referenced.
NovaGen Therapeutics
Pharmaceutical
Challenge
Screen failure rates of 38% in Phase III T2D+CKD comorbidity trial due to poorly calibrated inclusion criteria for multi-biomarker thresholds.
Solution
Generated 5,000 synthetic cohorts matching target demographics across FBC, renal, and metabolic panels. Simulated biomarker distributions for T2D + CKD comorbidity to stress-test inclusion/exclusion thresholds before patient recruitment.
Projected Outcome
Projected screen failure reduction to 18% by identifying that the original eGFR threshold excluded 22% of otherwise eligible participants. Saved an estimated 14 weeks in recruitment timeline.
Meridian Biopharma
Pharmaceutical
Challenge
Global pharmacovigilance NLP pipeline failed to extract adverse event signals from pathology reports in 8 APAC languages, delaying regulatory submissions.
Solution
Generated multilingual synthetic pathology reports across 74 languages, focusing on 12 Tier-1 languages with full medical term translations, to build a test corpus for their NLP extraction pipeline.
Projected Outcome
Identified 23 extraction failures in Vietnamese, Korean, and Tamil reports before go-live. Achieved 97.2% entity extraction accuracy across all APAC target languages, enabling on-time PMDA and TGA submissions.
Orion Clinical Research
Clinical Research Organisation
Challenge
Single-arm rare disease trial (n=47) lacked a matched control arm, making regulatory-grade statistical comparison impossible without an external comparator.
Solution
Generated 2,000 synthetic control profiles matching the trial's age/gender distribution with healthy + disease-adjacent condition profiles across all 7 panels. Adversarial testing validated distributional fidelity.
Projected Outcome
Synthetic external control arm accepted by biostatistics review committee. Enabled hazard ratio analysis that would have otherwise required a 200-patient parallel-group design, saving an estimated $4.2M in recruitment costs.
Nexus Health Analytics
Clinical Research Organisation
Challenge
CRO analysts waited 6-12 months for ethics committee approvals and data access agreements before they could begin developing real-world evidence (RWE) models.
Solution
Built synthetic data sandboxes with population cohorts mirroring client demographics. Analysts developed, validated, and iterated on RWE models using 10,000-profile synthetic datasets while data access was being negotiated.
Projected Outcome
Reduced model development lead time by 8 months. When real data became available, pre-validated model architecture required only 2 weeks of calibration, cutting total project delivery from 18 months to 10.
Supported Panels
9 panels covering 44 biomarkers with Australian reference ranges (RCPA, AIHW, Heart Foundation).
Full Blood Count
7Hb, WBC, Platelets, RBC, MCV, MCH, Haematocrit
Lipid Profile
6Total Chol, LDL, HDL, Triglycerides, ApoB, Lp(a)
Liver Function
6ALT, AST, GGT, ALP, Bilirubin, Albumin
Renal / Electrolytes
6Creatinine, eGFR, Urea, Sodium, Potassium, Uric Acid
Thyroid Function
3TSH, Free T4, Free T3
Iron Studies
4Serum Iron, Ferritin, Transferrin Sat, TIBC
Metabolic / Diabetes
3HbA1c, Fasting Glucose, Fasting Insulin
Vitals / Anthropometrics
5Systolic BP, Diastolic BP, Heart Rate, BMI, Waist Circ.
Bone & Mineral
4Calcium, Phosphate, Magnesium, Vitamin D
Ready to generate synthetic health data?
Schedule a demo to see how the Synthetic Health Data Engine can accelerate your clinical trials, pharmacovigilance testing, and real-world evidence workflows.