GeneEcho Knowledge Architecture

Session 4 Deliverable | AI-Accelerated Entrepreneurship Practicum

1. Core Entity Ontology

Defining the nodes that make up GeneEcho's "Elephant Memory" (Entity-Centric Learning).

Patient Relative Condition Biomarker Intervention Environment Payer

2. Domain Knowledge Graph (The Customer Zero Story)

How the AI reads the generational story to prevent late-stage diagnosis. These triples form the logic engine for GeneEcho's predictive capabilities.

[12_Year_Old_Patient] → (has_blood_relative) → [Grandmother]
[Grandmother] → (diagnosed_with) → [Severe_Myopia]
[12_Year_Old_Patient] → (logs_symptom) → [Squinting]
[GeneEcho_AI] → (detects_pattern) → [High_Risk_Myopia]
[GeneEcho_AI] → (recommends_intervention) → [Orthokeratology]
[12_Year_Old_Patient] → (benefits_from) → [Orthokeratology]
[Insurance_Provider] → (avoids_future_claim) → [Vision_Loss_Treatment]

3. Risk Analysis: "What We Don't Know"

Entity Resolution Risks (The Senzing Problem)

Ambiguous Matches: When hospital EHRs list "Peggy Smith" but the user's family tree says "Margaret Smith", the graph link breaks. We need fuzzy matching and entity-centric learning to resolve identities.

The HIPAA Gap: We don't know the exact legal mechanism to pull a grandmother's official EHR into a granddaughter's predictive model without direct consent from the grandmother.

Mitigation Strategy: GeneEcho will rely on user-attested family history (self-reported by the parent/child) to build the initial graph, circumventing EHR friction until direct API access and multi-party consent protocols are established.