synthea-pydantic 0.1.1

Type-safe Pydantic models for Synthea health data CSV exports

synthea-pydantic

Type-safe Pydantic models for parsing and validating Synthea's synthetic healthcare data CSV exports.

Overview

synthea-pydantic provides lightweight, type-annotated Pydantic models that make it easy to work with Synthea's CSV output format in Python. Synthea is a synthetic patient generator that creates realistic (but not real) patient health records for research, education, and software development.

Key Features

• 🏥 Complete Coverage: Models for all 19 Synthea CSV export types
• 🔍 Type Safety: Full type annotations with proper validation
• 🚀 Easy to Use: Simple API that works with standard CSV libraries
• 📋 Well Documented: Comprehensive field descriptions from Synthea specifications
• 🔧 Flexible: Handles optional fields and empty values gracefully
• ⚡ Lightweight: Minimal dependencies (just Pydantic)

Installation

pip install synthea-pydantic

Or with uv:

uv pip install synthea-pydantic

Quick Start

import csv
from synthea_pydantic import Patient, Medication, Condition

# Load patients from CSV
with open('patients.csv') as f:
    reader = csv.DictReader(f)
    patients = [Patient(**row) for row in reader]

# Access patient data with full type safety
for patient in patients:
    print(f"{patient.first} {patient.last} - Born: {patient.birthdate}")
    if patient.deathdate:
        print(f"  Died: {patient.deathdate}")

# Load related data
with open('medications.csv') as f:
    reader = csv.DictReader(f)
    medications = [Medication(**row) for row in reader]

# Filter medications for a specific patient
patient_meds = [m for m in medications if m.patient == patient.id]

Supported Models

synthea-pydantic includes models for all Synthea CSV export types:

Model	Description	Key Fields
Patient	Patient demographics	id, birthdate, name, address, ssn
Encounter	Healthcare encounters	id, patient, start/stop, type, provider
Condition	Medical conditions	patient, code, description, onset
Medication	Prescriptions	patient, code, description, start/stop
Observation	Clinical observations	patient, code, value, units
Procedure	Medical procedures	patient, code, description, date
Immunization	Vaccination records	patient, code, date
CarePlan	Treatment plans	patient, code, activities
Allergy	Allergy records	patient, code, description
Device	Medical devices	patient, code, start/stop
Supply	Medical supplies	patient, code, quantity
Organization	Healthcare facilities	id, name, address, phone
Provider	Healthcare providers	id, name, speciality, organization
Payer	Insurance companies	id, name, ownership
PayerTransition	Insurance changes	patient, payer, start/stop
Claim	Insurance claims	id, patient, provider, total
ClaimTransaction	Claim line items	claim, type, amount
ImagingStudy	Medical imaging	patient, modality, body_site

Usage Examples

Loading CSV Data

The models work with Python's built-in csv module:

import csv
from synthea_pydantic import Patient

# Load from CSV file
with open('data/patients.csv') as f:
    reader = csv.DictReader(f)
    patients = [Patient(**row) for row in reader]

Working with Optional Fields

Synthea CSVs often have empty values. The models handle these gracefully:

# Empty strings in CSV are converted to None
patient = Patient(**{
    'Id': '123e4567-e89b-12d3-a456-426614174000',
    'BIRTHDATE': '1980-01-01',
    'DEATHDATE': '',  # Empty string becomes None
    'PREFIX': '',     # Empty string becomes None
    'FIRST': 'John',
    'LAST': 'Doe',
    # ... other required fields
})

assert patient.deathdate is None
assert patient.prefix is None

Type Validation

All fields are validated according to their types:

from decimal import Decimal
from datetime import date, datetime
from uuid import UUID

# UUIDs are automatically parsed
assert isinstance(patient.id, UUID)

# Dates are parsed from YYYY-MM-DD format
assert isinstance(patient.birthdate, date)

# Decimals maintain precision for monetary values
assert isinstance(patient.healthcare_expenses, Decimal)

Linking Related Data

Use the UUID foreign keys to link related records:

# Find all medications for a patient
patient_meds = [
    med for med in medications 
    if med.patient == patient.id
]

# Find all conditions treated in an encounter
encounter_conditions = [
    cond for cond in conditions 
    if cond.encounter == encounter.id
]

Error Handling

The models provide clear error messages for invalid data:

try:
    patient = Patient(**invalid_data)
except ValidationError as e:
    print(f"Validation failed: {e}")

Model Details

Common Field Types

• IDs: UUID fields for primary and foreign keys
• Dates: date fields for dates (YYYY-MM-DD)
• Timestamps: datetime fields for date/time values
• Money: Decimal fields for monetary amounts
• Codes: String fields for medical codes (SNOMED-CT, RxNorm, etc.)

Base Model Features

All models inherit from SyntheaBaseModel which provides:

• Automatic whitespace stripping
• Empty string to None conversion
• Case-insensitive literal field matching
• Field alias support for CSV column mapping

Development

Setup

To develop or contribute to synthea-pydantic:

# Clone the repository
git clone https://github.com/yourusername/synthea-pydantic.git
cd synthea-pydantic

# Install in development mode
uv pip install -e .

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=synthea_pydantic

# Run specific test file
uv run pytest tests/test_patients.py

Code Quality

# Type checking
uv run mypy synthea_pydantic/

# Linting
uv run ruff check

# Formatting
uv run ruff format

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Synthea - The synthetic patient generator
• Pydantic - Data validation using Python type annotations

Resources

• Synthea Documentation
• Synthea CSV Format
• Sample Synthea Data

Citation

Synthea is a registered trademark of The MITRE Corporation.

Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079