Makerere University • FEM-AI HEALTH

Precision Reproductive Health Through Graph-Based Reasoning

A privacy-preserving, cycle-indexed platform that abandons generic calendar tracking for individualized, explainable conception probability estimation.

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About the Platform

What is Conceive?

Conceive is an advanced computational fertility framework delivered as a microservice API. It harmonizes diverse demographic, physiological, hormonal, medical, and lifestyle data into a unified predictive engine. By combining heterogeneous knowledge graphs, temporal transformers, and causal inference, it generates highly personalized cycle-level conception probabilities and individualized treatment effects.

Clinicians & Providers

Evidence-based decision-support for fertility planning

Digital Health Platforms

API-driven predictive intelligence for B2B partners

Women & Families

Actionable, personalized reproductive health insights

Heterogeneous Knowledge Graphs

Biological and behavioral realities mapped as causal relationships

Temporal Transformer Encoding

Longitudinal dynamics across a patient's cycle history

Causal Inference & ITE

Individualized treatment effects via T-Learner + SCM

Explainable AI (SHAP)

Additive feature attributions for clinical accountability

20,000 Cycle Records Curated from 50 anonymized patients

0.77 Validation F1-Score Significant reliability in predicting outcomes

4 Foundational Pillars Knowledge Graphs, Transformers, Causal Inference, SHAP

The Problem We Solve

Imprecise Tracking

Traditional fertility tools and ovulation kits fail to integrate heterogeneous biomedical and lifestyle data. Up to 30% of women rely on informal, inaccurate tracking methods.

Black-Box Algorithms

Existing digital solutions lack temporal granularity, ignore structural biological relationships, and operate as opaque "black boxes" that cannot explain their predictions.

Limited Causal Reasoning

Patients and clinicians need to know why a probability exists and what interventions could realistically alter it. Current models lack counterfactual analysis.

Why Uganda, Why Now

Our Strategic Market Opportunity

Reproductive health is a critical pillar of public health in Uganda and across Africa. Yet, specialized fertility diagnostics and clinical consultations remain inaccessible or prohibitively expensive for the majority of the population.

By engineering this computational framework locally, we provide a highly scalable, inclusive, and culturally aware fertility intelligence system. Conceive transforms the mobile device into a clinical-grade decision-support tool, ensuring that African institutions and women can access ethical, bias-audited reproductive AI without relying on synthetic assumptions from imported platforms.

Locally Engineered Culturally Aware Bias-Audited Clinically Validated

Impact at a Glance

30% of women rely on inaccurate tracking

20,000 cycle-level records validated

0.77 validation F1-score

4 pillars of computational architecture

Founders & Strategic Leads

Nawonga Catherine

Co-Founder & Technical Lead

Specializing in graph neural networks, causal inference, and real-time API deployment architectures. Leads the engineering of the Conceive Score API.

nawongacathy@gmail.com

Nakalembe Josephine

Co-Founder & Clinical Data Architecture Lead

Specializing in biomedical data science, clinical decision support systems, and ethical AI in healthcare. Oversees clinical validation and data governance.

Contact via team

Ggaliwango Marvin

Strategic Advisor & AI Mentor

Providing oversight on AI education, research-to-product commercialization, and responsible innovation. Academic Supervisor and Mentor.

Makerere University

Ready to Transform Reproductive Health Intelligence?

Join us in building ethical, explainable, and locally grounded fertility AI for Africa and beyond.