Canonical Pydantic data models for AshMatics healthcare applications - FDA vocabulary, regulatory schemas, and clinical AI use cases
Project description
AshMatics Core DataModels
Version: 0.3.1
Canonical Pydantic data models for AshMatics healthcare applications.
Changelog
v0.3.1 (2026-01-25) — ASHKBAPP-66
- Added
PROCESS_DOCUMENTATIONtoGovernanceCategoryenum - This is a core CAI framework category required for MCP service compatibility
Overview
This library provides the single source of truth for data contracts across the AshMatics ecosystem:
- Knowledge Base (KB)
- CoreApp
- ashmatics-tools SDK
- AI Watch applications
Features
- FDA Vocabulary: OpenFDA-aligned schemas for manufacturers, clearances, classifications, recalls, adverse events
- MongoDB Document Schemas: Three-tier structure for all
kb_*collections (evidence, regulatory, model cards, products, manufacturers, use cases) - Governance Document Models: Clinical AI Governance Framework artifacts (policies, SOPs, work products, process documentation)
- Use Case Taxonomy: Clinical AI use case categorization
- Rich Validation: Built-in validators for regulatory identifiers (K numbers, product codes)
- Database Agnostic: Pure Pydantic models, no ORM coupling
- Type Safe: Full type hints with mypy support
Installation
# From git (recommended for now)
pip install git+https://github.com/AsherInformatics/ashmatics-core-datamodels.git
# Or add to pyproject.toml
# dependencies = [
# "ashmatics-datamodels @ git+https://github.com/AsherInformatics/ashmatics-core-datamodels.git",
# ]
Quick Start
from ashmatics_datamodels.fda import (
FDA_ManufacturerBase,
FDA_510kClearance,
FDA_DeviceClass,
ClearanceType,
)
# Create a manufacturer
manufacturer = FDA_ManufacturerBase(
manufacturer_name="Medical AI Corp",
applicant="Medical AI Corp",
)
# Create a 510(k) clearance with validation
clearance = FDA_510kClearance(
k_number="K240001", # Validated format
clearance_date="2024-08-15",
device_name="AI-Chest Scanner",
device_class=FDA_DeviceClass.CLASS_2,
)
Package Structure
ashmatics_datamodels/
├── common/ # Base models, validators, regulators, frameworks
├── fda/ # FDA vocabulary (manufacturers, clearances, classifications, recalls, adverse events)
├── documents/ # MongoDB document schemas (three-tier structure)
├── use_cases/ # Clinical AI use case taxonomy
└── utils/ # Parsing and normalization utilities
Documentation
📚 Full Documentation (when published)
Or build locally:
uv pip install -e ".[docs]"
uv run mkdocs serve
Design Documents
License
Apache 2.0 - See LICENSE for details.
Contributing
This is an internal Asher Informatics library. For questions, contact info@asherinformatics.com.
Project details
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