A Synthea-inspired hybrid synthetic patient record generator. Learns the joint distribution of real anonymized Turkish-cohort EHR episodes with a Gaussian copula, then layers Synthea-style clinical pathways on top to emit fully-coded FHIR R4 bundles in Turkish.
syntha is a Python library, command-line tool, and signed cross-platform desktop app for generating realistic synthetic patient records — flat CSVs and FHIR R4 transaction Bundles — that match the statistical structure of an anonymized Turkish-cohort EHR while staying physiologically valid and clinically coded.
The pipeline is hybrid:
- Gaussian copula fitted on real anonymized episodes — preserves marginal distributions (age, labs, vitals, comorbidity prevalence) and their joint correlation structure.
- Physiologic filter — rejects samples that violate pulse-pressure, Friedewald lipid coherence, or eGFR ↔ creatinine constraints.
- Synthea-style clinical modules — nine condition-specific state activations that emit Encounters, MedicationRequests (RxNorm-coded), Procedures, and CarePlans matching each patient's comorbidity profile.
- FHIR R4 export — Patient + Observation + Condition + Encounter + MedicationRequest + Procedure + CarePlan + DiagnosticReport + RiskAssessment + FamilyMemberHistory, dual-coded LOINC / SNOMED CT / ICD-10 / RxNorm, Turkish locale (names, addresses, language code, display text).
A Tauri 2 app bundling the trained Gaussian copula. Picks cohort + n + seed + constraints, samples synthetic patients fully client-side (no Python at runtime), downloads a CSV. macOS DMG is Developer-ID signed + notarized + stapled. Windows installer is code-signed. All three OSes ship a minisign-signed auto-updater — existing installs get an in-app upgrade banner on next launch.
Install URLs auto-resolve to the latest release via releases/latest/download/… — no per-version link maintenance.
# PyPI
pip install syntha-ehr
# Or from source
git clone https://github.com/ArioMoniri/syntha
cd syntha
pip install -e ".[dev]"
# Or Docker
docker pull ghcr.io/ariomoniri/syntha:latest# Generate 1 000 synthetic episodes + FHIR bundles + model card + validation report
syntha generate \
--input data/raw/pristine_tolerant_episodes.csv \
--output output/tolerant \
--n 1000 --cohort tolerant
# Longitudinal — multiple encounters per patient with shared HASTA_ID
syntha generate \
--input data/raw/pristine_tolerant_episodes.csv \
--output output/tolerant_long \
--n 2000 --cohort tolerant \
--longitudinal --encounters-per-patient 4 --years-of-history 3
# Validate a synthetic CSV against the source it was trained on
syntha validate \
--source data/raw/pristine_tolerant_episodes.csv \
--synthetic output/tolerant/synthetic_tolerant_episodes.csv \
--output output/tolerant/validation.json
# Run a privacy audit (MIA + AIA)
syntha audit \
--source data/raw/pristine_tolerant_episodes.csv \
--synthetic output/tolerant/synthetic_tolerant_episodes.csv \
--output output/tolerant/privacy.jsonBy default the CSV writer drops 29 source-pipeline curation flags (pristine_*, berturk_*, drug-safety filters, rf_*) — those are training metadata, not clinical observations, and most are degenerate (constant 0 or 1) in the pristine cohort. Pass --curation-flags to keep them for QA work.
For every synthetic patient, syntha emits a FHIR R4 transaction Bundle:
| Resource | Coding | What |
|---|---|---|
| 👤 Patient | — | Turkish HumanName + Address (ISO 3166-2:TR province), communication.language = tr, derived birthDate |
| 🧪 Observation ×~12 | LOINC | Labs (glucose, full lipid panel, CBC, LFTs, eGFR/creatinine, ferritin, B12) + vitals (BP) |
| 🩺 Condition ×N | SNOMED CT + ICD-10 | Every active comorbidity, dual-coded, with English + clinical-Turkish display |
| 🏥 Encounter ×M | SNOMED CT | One per active condition, fired by the relevant module |
| 💊 MedicationRequest ×P | RxNorm | First-line therapy per condition, with dosage |
| 🔬 Procedure ×Q | SNOMED CT | HbA1c, lipid panel, ECG, spirometry, etc. |
| 📋 CarePlan ×R | SNOMED CT | Disease-specific lifestyle + monitoring plans |
| 📊 DiagnosticReport | LOINC | Lipid, CBC, CMP, iron, BP panels grouping their constituent Observations |
| 🎯 RiskAssessment | SNOMED CT | Charlson Comorbidity Index |
| 👪 FamilyMemberHistory | SNOMED CT | When rf_kanser / rf_kronik_hastalik are set |
…plus a flat CSV matching the input schema (minus the 29 dropped curation flags) for drop-in use as training data, a JSON model card with the source_sha256 and marginals, and a validation report.
A 100-episode sample of tolerant vs the full 135 569-row source:
| Metric | Value |
|---|---|
| n source / synthetic | 135 569 / 100 |
| Max Kolmogorov–Smirnov across continuous columns | 0.14 |
| Mean KS | 0.07 |
| Max binary-prevalence error | 0.025 (has_rx_data) |
| Disease-prevalence error (HTN / DM / hyperlipidemia) | 0.015 / 0.004 / 0.010 |
| Spearman correlation Frobenius diff | 2.94 |
| Fraction of synthetic patients with all labs in reference range | reported per cohort in validation_report.json |
# Spin up a local read-only FHIR R4 server
syntha serve --bundles examples/sample_output/sample_bundles.ndjson --port 8080
# Then:
curl http://127.0.0.1:8080/metadata # CapabilityStatement
curl http://127.0.0.1:8080/Patient # search-set Bundle
curl http://127.0.0.1:8080/Patient/{id}
curl http://127.0.0.1:8080/\$export # Bulk Data NDJSONscripts/post_to_fhir.sh posts every transaction Bundle in an NDJSON file to any FHIR R4 endpoint (default: the public HAPI test server).
The trained models bundled with the desktop app and the example output come from pristine_strict_episodes.csv and pristine_tolerant_episodes.csv — anonymized retrospective EHR episodes from a Turkish patient cohort selected to represent clinically pristine adults. The source CSVs themselves are gitignored and never redistributed.
The output is Turkish-localized:
- Patient names sampled from Turkish given-name and family-name distributions (
src/syntha/locale/turkish.py). - Addresses use Turkish cities weighted by approximate population with ISO 3166-2:TR province codes.
- Every Condition carries both an English SNOMED display and a clinical-Turkish translation in
Condition.code.text. Patient.communication.languageistr.
All clinical terminology used (LOINC, SNOMED CT, ICD-10, RxNorm) comes from open international standards. No licensed terminology content is embedded.
Nine modules ship out of the box (src/syntha/modules/); each fires on its corresponding comorbidity flag.
| Module | Source flag(s) | Emits |
|---|---|---|
| 🫀 Hypertension | Hipertansiyon |
Encounter, 1–2 antihypertensives (stage 2 → dual), CarePlan |
| 🍬 Diabetes | DM_Tum, DM_Komplikasyonlu |
Encounter, HbA1c, metformin (+ insulin if severe), CarePlan |
| 🧀 Hyperlipidemia | Hiperlipidemi |
Encounter, lipid panel, statin (high-intensity if LDL ≥ 190) |
| 🦋 Thyroid | Tiroid |
Encounter, TSH, levothyroxine |
| 😔 Depression | Depresyon |
Psych encounter, sertraline, CBT CarePlan |
| 😰 Anxiety | Anksiyete |
Psych encounter, escitalopram (or buspirone if already on an SSRI) |
| ❤️ Ischemic heart disease | Iskemik_Kalp |
Cardiology encounter, ECG, aspirin + β-blocker + statin |
| 🌬️ Asthma | Astim |
Resp encounter, spirometry, SABA + ICS |
| 🚭 COPD | COPD |
Resp encounter, spirometry, LABA + SABA |
Module authoring guide: docs/MODULES.md.
┌──────────────┐ ┌──────────────────┐ ┌──────────────────────┐
│ Source CSV │──▶│ Gaussian copula │──▶│ Physiologic filter │
│ (Turkish │ │ (mixed-type ρ; │ │ (BP, Friedewald, │
│ pristine) │ │ nearest-PSD) │ │ eGFR ↔ creatinine) │
└──────────────┘ └──────────────────┘ └─────────┬────────────┘
│
┌────────────────────┴────────────────────┐
│ │
▼ ▼
┌──────────────────┐ ┌──────────────────────────┐
│ Longitudinal │ (optional) │ Single-encounter CSV + │
│ expansion │ ───────────────▶│ FHIR R4 export with │
│ (drift, Poisson) │ │ module activation │
└─────────┬────────┘ └──────────────────────────┘
│
▼
(same FHIR export)
Full math (mixed-type correlation, nearest-PSD projection, conditional missingness, AR(1) lab drift): docs/ARCHITECTURE.md.
| Command | What |
|---|---|
syntha generate |
End-to-end: train copula → sample → modules → CSV + FHIR + model card + validation |
syntha fit |
Fit and persist a copula in a registry without sampling |
syntha sample |
Raw sampling from a registered model |
syntha sample-conditional |
AST-validated rejection sampling against a pandas filter expression |
syntha fhir |
Convert an existing synthetic CSV to FHIR R4 bundles |
syntha validate |
KS / Wasserstein / correlation diff + reference-range coverage |
syntha audit |
Privacy audit (membership-inference + attribute-inference) |
syntha serve |
Read-only FHIR R4 demo server |
syntha export-model |
Export a registered copula to v2 JSON for the desktop app |
syntha list-models, show-card |
Inspect the registry |
Run syntha <cmd> --help for full option lists.
A pretty-printed sample Bundle, a 100-episode synthetic CSV, the model card, and the validation report all live under examples/sample_output/ and are tracked in git.
| File | What |
|---|---|
sample_bundle_pretty.json |
One pretty-printed transaction Bundle |
sample_bundles.ndjson |
100 Bundles, one per line (Bulk-FHIR style) |
sample_episodes.csv |
100 synthetic episodes matching the input schema |
sample_model_card.json |
source_sha256, n_train, marginals, top correlations |
sample_validation_report.json |
KS / Wasserstein / correlation-Frobenius per column |
For FHIR-aware rendering: drop the Bundle onto simplifier.net or the HL7 Clinical FHIR Renderer.
- Not privacy-proof. Gaussian copulas are not differentially private. Run
syntha auditbefore sharing any synthetic dataset trained on a small or sensitive cohort. - Not a substitute for real PHI when validity hinges on rare events — the copula reproduces the bulk of the joint distribution, not the long tails.
- Not a population-representative Turkish cohort by default — the source is selected for clinically-pristine adults, so synthetic disease prevalence is lower than TÜİK national figures. Calibration to TÜİK is a curation task — see ROADMAP.md and COLLABORATE.md for how to help.
Open-source, Apache 2.0, contributions welcome from clinicians, data scientists, and software engineers alike. Three places to start:
- 🧑⚕️ Clinicians — see COLLABORATE.md for the live list of tasks needing clinical-Turkish guidance (drug calibration, ICD specificity, new modules), plus the in-app Collaborate panel that surfaces the same list with one-click "claim" via your GitHub handle.
- 💻 Developers — CONTRIBUTING.md for dev setup, commit conventions, and the test matrix.
- 🗺️ Project direction — ROADMAP.md for the staged plan, what's shipped, and what's queued.
Apache 2.0 © 2026 Ariorad Moniri — see LICENSE. If you use syntha in academic work, please cite:
Moniri, A. (2026). syntha: hybrid synthetic patient record generator
trained on Turkish pristine-healthy EHR cohorts.
https://github.com/ArioMoniri/syntha
| Project | What it gives us | |
|---|---|---|
| 🩺 | Synthea | Inspiration for the clinical-module layer and FHIR output format |
| 🧪 | LOINC | Lab and observation codes |
| 🧬 | SNOMED CT | Condition, procedure, encounter, and care-plan terminology |
| 📑 | ICD-10 | Diagnosis coding alongside SNOMED |
| 💊 | RxNorm | Medication coding |
| 📊 | Turkish-cohort EHR data steward | De-identified retrospective episodes (anonymized upstream; never redistributed by this repo) |
|
💬 Discussions
Open questions, "is this the right tool for X?", show-and-tell |
🐛 Issues
Bug reports + feature requests + clinical curation |
🤝 Collaborate
Live list of clinician + dev + data tasks · also surfaced in the desktop app |
|
📖 Contributing Dev setup, commit conventions, test matrix |
🗺️ Roadmap Shipped + queued + what needs a clinician |
📋 Changelog Semver, Keep-a-Changelog, generated by release-please |