Solution Architecture
Graph-temporal-causal fusion for explainable, equitable fertility intelligence.
Graph-Temporal-Causal Fusion
To solve the clinical crisis, Conceive engineers a multi-modal pipeline that unifies mechanistic biology, temporal tracking, and structural graph reasoning. Our computational architecture is built upon four foundational pillars that work in concert to deliver explainable, equitable, and clinically actionable fertility intelligence.
Four Foundational Pillars
The Core of Our Computational Architecture
Heterogeneous Knowledge Graphs
We map biological and behavioral realities by constructing a custom knowledge graph encoding the causal relationships between biomarkers (e.g., Estradiol, Progesterone), diagnoses (e.g., PCOS), treatments, and outcomes. Using Relational Graph Convolutional Networks (RGCNs), the system learns latent representations of these complex interactions.
Temporal Transformer Encoding
Because fertility is a sequential, cycle-by-cycle process, we utilize time-series transformers to capture longitudinal dynamics and long-range dependencies across a patient's cycle history. This enables the model to understand patterns that span multiple cycles.
Causal Inference & ITE
We do not stop at prediction. By layering a T-Learner meta-learner over a Structural Causal Model (SCM), our engine estimates counterfactuals. The system simulates how modifiable lifestyle factors or clinical treatments will directly uplift the individual's conception probability.
Explainable AI (SHAP) & Fairness
Black-box medicine is unethical. Conceive utilizes SHAP to provide additive feature attributions, immediately highlighting which specific hormonal thresholds or lifestyle features are driving the prediction. Rigorous subgroup calibration audits ensure the model remains fair and reliable.
Technical Architecture
How It All Works Together
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).
Microservice Architecture
Privacy-preserving FastAPI backend for seamless integration
Continuous Drift Monitoring
MLflow integration for real-time model performance tracking
Model Cards
Transparent documentation of model capabilities and limitations
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