Methodology

Research-Grade Synthetic Data Generation

A multi-stage pipeline combining expert clinical knowledge, state-of-the-art generative AI, and rigorous privacy preservation to produce datasets indistinguishable from real clinical data.

99.2%

Clinical Validity

ε<0.8

Differential Privacy

0.52

MIA Resistance (AUC)

Why Synthetic Mental Health Data?

The Privacy Paradox

Mental health data is among the most sensitive information that exists. Traditional datasets risk re-identification, stigma, and discrimination. 21% of AI projects fail due to privacy concerns alone.

The Data Scarcity Crisis

Rare conditions like mixed bipolar states, DID, and anti-NMDAR encephalitis have insufficient research data. IRB approvals take 6-18 months. HIPAA compliance adds massive overhead.

The Synthetic Solution

Our datasets provide 100% privacy (no real patients), immediate availability (no IRB), and unlimited sharing (no data agreements). Research without compromise.

Six-Stage Generation Pipeline

Every MentalData dataset undergoes a rigorous six-stage process, combining human expertise with machine learning to ensure both clinical validity and mathematical privacy guarantees.

Stage 1                Stage 2                Stage 3
+------------------+      +------------------+      +------------------+
|   Expert Seed    |      |  Schema Design  |      | CTGAN Training  |
|     Curation     | ---> |   & Constraints | ---> |   with DP-SGD   |
|  (30 patients)  |      |  (DSM-5-TR)     |      |  (300 epochs)   |
+------------------+      +------------------+      +------------------+
                                                            |
        +---------------------------------------------------+
        |
        v
Stage 4                Stage 5                Stage 6
+------------------+      +------------------+      +------------------+
|    Sampling &    |      |   Clinical      |      |  Triple-Layer   |
|   Privacy Layer  | ---> |  Enrichment    | ---> |   Validation    |
| (k=12, ε<0.8)   |      | (Meds, AEs)    |      | (Stat+Clin+Priv)|
+------------------+      +------------------+      +------------------+

1

Expert Seed Curation

Mental health professionals create foundational seed datasets (n=30 patients, 7 longitudinal visits) representing realistic clinical trajectories. Each seed covers all diagnostic subcategories, treatment response patterns, and demographic diversity.

  • Expert psychiatrists design clinical scenarios
  • Full coverage of ICD-10/DSM-5-TR codes
  • Realistic treatment progression patterns

2

Schema Design & Clinical Constraints

We define valid ranges, inter-variable relationships, and clinical business rules based on DSM-5-TR criteria and published literature. The schema ensures generated data cannot violate medical reality.

  • YMRS range: 0-60 (Young Mania Rating Scale)
  • HAM-D range: 0-52 (Hamilton Depression Scale)
  • Diagnosis-symptom coherence rules
  • Medication-dose therapeutic windows

3

CTGAN Training with DP-SGD

Our Conditional Tabular GAN learns the joint distribution of clinical variables while Differentially Private Stochastic Gradient Descent ensures mathematical privacy guarantees from the ground up.

  • 300 epochs with convergence monitoring
  • Mode-specific normalization for mixed data types
  • Gradient clipping (max_norm=1.0) + noise injection
  • Privacy budget: ε<0.8, δ=10-5

4

Sampling & Privacy Enforcement

We generate the target population size and enforce k-anonymity on quasi-identifiers. Every equivalence class contains at least k=12 records, preventing singling out any individual.

  • Scale from seed to 800+ patients
  • k-Anonymity (k≥12) on age/sex/diagnosis
  • Suppression rate <5% to preserve utility
  • Zero real patient data in output

5

Clinical Enrichment

Expert-designed algorithms add medications, adverse events, and psychological features based on evidence-based treatment protocols and published prevalence rates.

  • Medication assignment per diagnosis severity
  • Adverse events linked to specific drugs (e.g., Lithium → tremor 30%)
  • Psychological features (identity crisis, sleep aversion)
  • Coherent polypharmacy patterns

6

Triple-Layer Validation

Every dataset passes statistical, clinical, and privacy validation before release. We measure fidelity, utility, and attack resistance using industry-standard metrics.

  • Statistical: KS tests, correlation preservation (>0.75)
  • Clinical: DSM-5-TR compliance (≥90%), expert review
  • Privacy: MIA resistance (AUC<0.55), attribute inference tests

Privacy-First Architecture

Mental health data demands the highest privacy standards. Our approach combines multiple defense layers, providing mathematical guarantees that no individual can ever be identified.

Differential Privacy

Mathematical Guarantee

DP-SGD training ensures ε<0.8 privacy budget. Even with unlimited queries, an attacker gains negligible information about any individual in the training data.

Pr[M(D1) ∈ S] ≤ eε × Pr[M(D2) ∈ S] + δ

K-Anonymity

Group Protection

Every combination of quasi-identifiers (age, sex, diagnosis) appears in at least k=12 records. No individual can be singled out from the crowd.

Result: All equivalence classes ≥12 records
Suppression: <5% of records affected

Attack Testing

Adversarial Validation

We actively attempt Membership Inference Attacks (MIA) on every dataset. Our target: AUC ≤ 0.52 (random guessing level). Attackers cannot determine if any individual was in training.

MIA AUC: 0.52 (achieved)
Interpretation: Equivalent to coin flip

Quality Standards & Grading

Every MentalData dataset receives a composite quality grade based on three pillars: Statistical Fidelity, Clinical Validity, and Privacy Protection.

A
90-100 Points
Publish-Ready
Excellent Quality
B
80-90 Points
Good Quality
Minor Caveats
C
70-80 Points
Acceptable
Documented Limitations
F
<70 Points
Not Released
Revision Required

Scoring Formula

Quality Score = (0.40 × Statistical) + (0.40 × Clinical) + (0.20 × Privacy)

Validation Thresholds

Category Metric Acceptable Excellent
Statistical Correlation Preservation >0.70 >0.90
Propensity AUC <0.65 <0.55
TSTR Utility >0.80 >0.95
Clinical DSM-5-TR Compliance ≥90% ≥95%
Expert Plausibility ≥85% ≥90%
Privacy MIA Resistance (AUC) <0.65 <0.55
Differential Privacy (ε) ≤10 ≤1.0

Technology Stack

Generative Models

  • CTGAN (Conditional Tabular GAN)
  • TVAE (Tabular VAE)
  • SDV Framework v1.15+

Privacy Layer

  • DP-SGD (Opacus)
  • ARX k-Anonymity
  • Custom MIA Testing Suite

Validation

  • SDMetrics Quality Reports
  • SciPy Statistical Tests
  • Clinical Rule Engine

Export Formats

  • CSV, Parquet, SQLite
  • HL7 FHIR R4, CDISC ODM-XML
  • Stata DTA, REDCap

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Scientific Foundation

  1. Xu, L., et al. (2019). Modeling Tabular Data using Conditional GAN. NeurIPS.
  2. Patki, N., et al. (2016). The Synthetic Data Vault. IEEE DSAA.
  3. Abadi, M., et al. (2016). Deep Learning with Differential Privacy. ACM CCS.
  4. Kotelnikov, A., et al. (2023). TabDDPM: Modelling Tabular Data with Diffusion Models. ICML.
  5. American Psychiatric Association. (2022). DSM-5-TR. Washington, DC.