GeneEcho: Knowledge Architecture

Founder: Jessica Ding | Course: AI-Accelerated Entrepreneurship Practicum (Session 4)

Training the AI brain of GeneEcho using Entity-Centric Learning and Triples.

Part 1: The 100-Triple Knowledge Graph

These triples define the core entities (Subject/Object) and relationships (Predicate) that power GeneEcho's preventative health engine across 5 data layers.

Part 2: Risk Analysis ("What We Don't Know")

Data Privacy Risk HIPAA Boundaries Across Family Trees

The Unknown: If a grandmother's genetic marker predicts a risk for the granddaughter, does revealing the granddaughter's risk implicitly violate the grandmother's privacy rights? We do not fully know the legal boundaries of "implied consent" in multigenerational AI mapping.

Data Integrity Risk The "Ambiguous Match" Problem

The Unknown: Following Senzing's entity resolution model, we assume we can resolve "Margaret Smith" in an EHR to "Peggy Smith" in a user's family tree. However, if data is entered incorrectly, GeneEcho might map a severe condition to the wrong relative, triggering false positives and unnecessary anxiety.

Behavioral Risk Wearable Compliance Gap

The Unknown: Users are inconsistent with wearables (like I am with my own). We do not know the exact "decay rate" of our predictive models when a user stops wearing their Apple Watch for 3 weeks. Does the AI pause, or does it guess?

Clinical Risk Correlation vs. Causation

The Unknown: Connecting environment (pollen/UV) with genetics and wearables might yield statistically significant patterns that are not medically actionable. We don't know the threshold where a "pattern" officially becomes a "medical recommendation" without facing FDA diagnostic regulation.