Health Tech
Policy-Embedded Graph Expansion: Networked HIV Testing with Diffusion-Driven Network Samples
HIV is a retrovirus that attacks the human immune system and can lead to death without proper treatment. Our research, in collaboration with WHO and Wits University, focuses on improving the efficiency of HIV testing to support UN Sustainable Development Goal 3.3. We introduce Policy-Embedded Graph Expansion (PEGE), a novel framework that embeds generative graph expansions into decision-making, and Dynamics-Driven Branching (DDB), a diffusion-based model for data-limited settings. This combined approach consistently outperforms existing baselines on real HIV transmission networks, showing significant improvements in discounted reward and HIV detection rates.
Executive Impact: Transforming HIV Testing Strategies
Policy-Embedded Graph Expansion (PEGE) and Dynamics-Driven Branching (DDB) deliver significant improvements in HIV testing efficiency, directly advancing global health goals.
0
Improved Discounted Reward
0
More HIV Detections (25% Tested)
0
AUC of FOG Achieved
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Health Tech
Public Health
AI for Social Good
1.3M+
New HIV Infections Annually (UNAIDS 2024)
Globally, 1 in 7 individuals with HIV are undiagnosed. Existing network-based testing approaches struggle with incomplete data, hindering efficient identification and prevention efforts.
Enterprise Process Flow: Policy-Embedded Graph Expansion (PEGE)
Observe Current State (s(t))
→
Sample 'k' Graph Expansions (PEGE+DDB)
→
Solve & Aggregate Oracle Evaluations
→
Agent Acts on Node 'a'
→
Observe Outcomes & New State (s(t+1))
Key Performance Indicators
PEGE + DDB demonstrates superior efficiency, crucial for resource-constrained environments.
0
AUC Boost vs. Baseline
0
Reward Gain (25% Budget)
| Feature | Traditional Network Methods | Policy-Embedded Graph Expansion (PEGE) |
|---|---|---|
| Network Observability | Requires full network data | Handles incrementally revealed, partial graphs |
| Graph Reconstruction | Explicit topological reconstruction | Directly embeds generative distribution |
| Data Requirements | Heavy data/supervision for training | Tailored for data-scarce settings (DDB) |
| Adaptability | Less adaptable to changing network size | Adaptable, tunable expansion depth |
| Real-world Relevance | Often impractical for deployment | Designed for practical, data-limited scenarios |
Driving Global Health Outcomes in South Africa
Our interdisciplinary team, in collaboration with WHO and Wits University in South Africa, is actively working to deploy PEGE + DDB in real-world clinics. This initiative directly supports UN Sustainable Development Goal 3.3 by enhancing HIV testing efficiency.
Challenges Addressed:
- Limited testing resources and staff availability.
- Difficulty for humans to identify efficient testing sequences.
- Need for intelligent algorithms adaptable to evolving network data.
Expected Outcomes:
- Enable earlier treatment and prevention for thousands of individuals.
- Contribute significantly to UNAIDS 95-95-95 targets by improving awareness.
- Provide a scalable, AI-driven solution for resource-constrained environments.
Impact on Public Health
PEGE and DDB offer a transformative approach to public health interventions, particularly in resource-limited settings. By optimizing HIV testing strategies, the framework accelerates the identification of positive cases, leading to earlier treatment, reduced transmission rates, and improved individual health outcomes. This directly supports global efforts to control epidemics and achieve health equity.
The ability to operate with partial network observability and limited training data makes it uniquely suited for real-world public health challenges where comprehensive data is often unavailable or expensive to collect.
AI for Social Good
This research exemplifies the powerful application of AI for social good. By leveraging advanced graph expansion models and decision-making policies, PEGE + DDB provides a crucial tool to address one of the most pressing global health challenges: HIV. The ethical deployment of AI in public health requires robust, adaptable, and efficient solutions like PEGE.
Our collaboration with international health organizations ensures that the technology is not only cutting-edge but also directly applicable and beneficial to communities most in need, driving tangible positive impact on human lives.
Calculate Your Potential ROI
Estimate the impact AI can have on your organization's operational efficiency and cost savings.
Estimated Annual Savings
Calculating...
Hours Reclaimed Annually
Calculating...
Your AI Implementation Roadmap
A clear, phased approach to integrating advanced AI into your operations for maximum impact.
Phase 1: Discovery & Strategy
In-depth analysis of your current workflows and strategic goals to identify optimal AI integration points.
Phase 2: Pilot & Development
Design and implement a tailored AI pilot program, ensuring seamless integration and initial validation.
Phase 3: Scaling & Optimization
Expand AI solutions across your enterprise, with continuous monitoring and optimization for peak performance.
Phase 4: Advanced Integration & Training
Leverage full AI capabilities with advanced features and comprehensive training for your teams, fostering self-sufficiency.
Ready to Transform Your Enterprise?
Schedule a free consultation with our AI experts to explore how Policy-Embedded Graph Expansion and other cutting-edge AI solutions can drive efficiency and impact in your organization.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat