API & Integration
Enterprise-grade fertility intelligence for digital health platforms and clinical workflows.
API-First Deployment
Conceive is not a standalone academic dashboard; it is designed for enterprise scalability. The predictive engine is deployed as a privacy-preserving microservice via a robust FastAPI backend. This allows seamless integration into existing digital health ecosystems, fertility tracking applications, and electronic health records (EHR).
MLOps Architecture
Enterprise-Grade Machine Learning Operations
FastAPI Backend
RESTful API with automatic OpenAPI documentation, input validation, and asynchronous request handling for low-latency responses.
Docker Containerization
Fully containerized deployment enabling consistent environments across development, staging, and production with horizontal scalability.
MLflow Drift Monitoring
Continuous tracking of model performance metrics with automated drift detection and scheduled retraining pipelines.
CI/CD Pipelines
GitHub-integrated automated testing, model validation, and deployment pipelines ensuring production reliability.
API Endpoints
Real-Time Fertility Intelligence at Your Fingertips
Get cycle-specific conception probability based on patient data.
{
"age": 32,
"bmi": 24.5,
"estradiol": 120,
"progesterone": 8.2,
"cycle_length": 28,
"pcos": false,
"endometriosis": false,
"stress_level": 3
}
Response:
{
"probability": 0.72,
"confidence_interval": [0.65, 0.79],
"cycle_day": 14
}
Run counterfactual simulations to see how interventions affect conception probability.
{
"patient_id": "P-2024-001",
"intervention": {
"diet_type": "low_gi",
"workout_type": "moderate"
}
}
Response:
{
"baseline_probability": 0.45,
"simulated_probability": 0.62,
"uplift": 0.17,
"treatment_effect": "positive"
}
Receive SHAP feature attributions explaining why a specific prediction was made.
{
"features": [
{"name": "Progesterone", "value": 8.2, "shap": 0.35},
{"name": "Estradiol", "value": 120, "shap": 0.28},
{"name": "Age", "value": 32, "shap": -0.12}
]
}
Access real-time fairness metrics and subgroup calibration data for clinical auditing.
{
"subgroup_calibration": {
"age_<30": {"calibration_error": 0.03},
"age_30-40": {"calibration_error": 0.04},
"age_>40": {"calibration_error": 0.05}
},
"bmi_quartiles": {"q1": 0.02, "q2": 0.03, "q3": 0.04, "q4": 0.05}
}
Quick Integration
Integrating the Conceive Score API into your digital health platform is straightforward. Here's a simple Python example to get you started.
import requests
import json
url = "https://api.conceive.health/v1/predict"
headers = {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
}
data = {
"age": 32,
"bmi": 24.5,
"estradiol": 120,
"progesterone": 8.2,
"cycle_length": 28,
"pcos": False,
"endometriosis": False,
"stress_level": 3
}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(json.dumps(response.json(), indent=2))Sign Up for API Access
Request your API key through the Join the Venture page
Integrate the Endpoint
Use the /predict endpoint with your patient data
Interpret Results
Use SHAP explanations and fairness metrics for clinical decisions
Why Integrate Conceive?
- Enterprise-grade predictive intelligence
- Privacy-preserving microservice
- SHAP explainability for clinical trust
- Fairness auditing built-in
- Continuous drift monitoring
- Uganda-built, locally governed
Ready to Integrate Conceive?
Upgrade your digital health platform from basic calendar tracking to graph-reasoning fertility intelligence.