Skip to content

Deploy ML Models as Healthcare APIs

This example is coming soon! 🚧

ML Model Deployment Architecture

Overview

This tutorial will demonstrate how to deploy any trained ML model as a production-ready healthcare API with FHIR input/output, multi-EHR connectivity, and comprehensive monitoring.

What You'll Learn

  • Model serving architecture - Deploy Hugging Face, scikit-learn, PyTorch, and custom models
  • FHIR-native endpoints - Serve predictions with structured healthcare data formats
  • Multi-EHR integration - Connect your model to live FHIR servers for real-time inference
  • Healthcare data validation - Ensure type-safe input/output with Pydantic models
  • Production monitoring - Track model performance, data drift, and API health
  • Scalable deployment - Configure auto-scaling and load balancing for healthcare workloads

Architecture

The example will showcase:

  1. Model Packaging - Wrap any ML model with HealthChain's deployment framework
  2. FHIR Endpoint Creation - Automatically generate OpenAPI-compliant healthcare APIs
  3. Real-time Inference - Process FHIR resources and return structured predictions
  4. Multi-source Integration - Connect to Epic, Cerner, and other FHIR systems
  5. Performance Monitoring - Track latency, throughput, and prediction quality
  6. Security & Compliance - Implement OAuth2, audit logging, and data governance

Use Cases

Perfect for: - Clinical Decision Support - Deploy diagnostic or prognostic models in EHR workflows - Population Health - Serve risk stratification models for large patient cohorts - Research Platforms - Make trained models available to clinical researchers - AI-powered Applications - Build healthcare apps with ML-driven features

Example Models

We'll show deployment patterns for: - Clinical NLP models - Named entity recognition, clinical coding, text classification - Diagnostic models - Medical imaging analysis, lab result interpretation - Risk prediction models - Readmission risk, mortality prediction, drug interactions - Recommendation systems - Treatment recommendations, medication optimization

Prerequisites

  • A trained ML model (any framework supported)
  • Understanding of FHIR resources and healthcare data standards
  • Python environment with HealthChain installed
  • Basic knowledge of API deployment concepts

Coming Soon

We're building comprehensive examples covering multiple model types and deployment scenarios!

In the meantime, explore our Gateway documentation to understand the deployment infrastructure.

Want to be notified when this example is ready? Join our Discord community for updates!