Vector Decision Engine
Real-time 3DQ state vector computation across disease activity, host competence, and therapeutic burden axes.
Decision intelligence supporting infectious disease platform development.
GEDM3DQ (Gradient Equilibrium Decision Modeling 3-D Quadrant) supports modeling, simulation, evidence organization, and decision logic, while biological validation, clinical review, and regulatory decisions remain separate.
GEDM3DQ structures complex biological state into decision context.
A clinician-facing decision layer for state modeling, uncertainty, and therapy-window interpretation.
Core Engine Architecture
Monte Carlo Simulator
Stochastic outcome simulations per decision point to quantify uncertainty.
Trajectory Optimizer
Patient state evolution modeling to identify optimal therapeutic windows.
Recommendation Generator
Clinician-facing therapy adjustments with confidence and explainability.
Cost-Risk Modeler
Multi-objective optimization balancing efficacy, safety, and economic cost.
GEDM3DQDecision Intelligence
3DQ State Space
- X Disease activity
- Y Host competence
- Z Therapeutic burden
6-Layer Architecture
- L1 Perception
- L2 Risk Integration
- L3 Memory
- L4 Equilibrium
- L5 Decision
- L6 Learning
Key Metrics
- MC Stochastic simulations
- 3 State space axes
- 5 Core engines
Core 6 Layers
The predictive framework of dynamic equilibrium.
Input
Multi-omic and EHR acquisition for observation of complexity.
Normalization
TEMC encoding and pattern recognition.
Modeling
Gradient Tensor E(D) mapping and equilibrium mapping.
Simulation
Monte Carlo forecasting for prediction and scenario analysis.
Decision
3-D risk-urgency-cost optimization for control and optimization.
Learning
Feedback and adaptive refinement for evolution, adaptation, and intelligence.
How It Works
Model, simulate, reason, recommend.
The engine is presented as a decision-support layer: it helps organize evidence, represent uncertainty, compare development paths, and support program prioritization.
Structured analysis works alongside experimental validation, clinical review, and regulatory decision-making.
Clinician Decision Support
Supports review across efficacy, safety, feasibility, and access.
GEDM3DQ organizes inputs for patient-selection hypotheses, safety review, manufacturing decisions, and access planning. Clinical judgment, ethics review, and regulatory decisions remain separate.
Efficacy
Evidence inputs for response hypotheses.
Safety
Signals for clinician review and monitoring.
Feasibility
Manufacturing and protocol planning inputs.
Access
Market and policy planning context.
Referenced support letter. A letter attributed to Uganda's Ministry of Health dated 28 Apr 2026 references the GEDM AI platform for treatment-optimization and clinical decision-support planning in a Wellcome Trust-aligned clinical program, subject to regulatory and ethics approvals.