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University Capstone · Virginia Tech

Personalized Health Digital Twin

A living, evolving computational model of a person’s health, built by a student team, grounded in real clinical science, and designed to change how people understand their own bodies.

Research / DesignVirginia TechAdvanced Genomics Research Inc.

At a Glance

Project Fact Sheet

Partner university

Virginia Tech, PBL Capstone / Integrated Project Design

Technology partner

Advanced Genomics Research Inc. (AGR)

Timeline

Fall 2026 (16-week semester), presented at VT PBL Pitch Day

Status

Research / Design: team kickoff and scoping

Focus areas

Healthcare Education · Emerging Technology Readiness

Audience

Patients, healthcare professionals and researchers

The Challenge

A person’s health is dynamic

Healthcare decisions are often based on periodic measurements, generalized population models and information collected during individual clinical visits. But a person’s health is dynamic. Medications, activity, sleep, diet, stress, hormonal changes and existing conditions interact over time in complex ways that a few checkups a year cannot capture.

Problem statementHow might we design and prototype a personalized health digital-twin platform that integrates individual health and physiological data to model changes in a person’s health over time and explore the potential effects of different behaviors, conditions, or interventions?

What is a digital twin?

A digital twin is a computational representation of a real thing (a jet engine, a factory, a person) that keeps evolving as new information arrives.

A health digital twin combines a person’s demographic and clinical information with physiological measurements to show their current state, how it is changing, and how it might respond to different behaviors or interventions.

Why It Matters in Virginia

The need is close to home

1 in 11

Virginians live with diabetes (12.9% of adults), and 1 in 4 of them don’t know it yet.

9.1%

Of Virginia adults report cardiovascular disease, still the state’s leading cause of death.

~1.12M

Women ages 45 to 64 in Virginia, the perimenopause and menopause transition window.

85,100+

Life-sciences professionals work in Virginia, backed by $13B in 2025 industry investment.

Sources: Virginia Department of Health (2025); America’s Health Rankings (2024); U.S. Census Bureau ACS 2019–2023; Virginia Economic Development Partnership (2025).

Use Cases

One platform, many health journeys

01

Diabetes

Models relationships among glucose levels, meals, activity and medication.

02

Menopause

Examines symptoms, sleep, activity and physiological change over time.

03

Cardiovascular health

Incorporates heart rate, blood pressure, activity and other indicators.

The platform is designed to extend beyond these three and, over time, to take in real-time data from wearable and connected medical devices.

What the Team Is Building

From data to insight

Step 1

Inputs

  • Demographic & clinical data (history, labs, encounters)
  • Physiological measurements (vitals, symptom tracking)
  • Wearable device streams (future extension)
Step 2

Digital twin engine

  • Integrate heterogeneous data
  • Model relationships over time
  • Represent uncertainty
Step 3

Outputs

  • Current-state view: where things stand today
  • Change-over-time detection: what’s shifting, and when
  • “What-if” exploration: effects of behaviors and interventions

Key challenges

01
Integrating data that doesn’t speak the same language
02
Modeling how glucose, sleep, stress and medication interact over time
03
Showing what the model doesn’t know yet
04
Protecting sensitive health information from day one
Timeline & Milestones

Semester roadmap

Weeks 1–2

Onboarding & scoping

Scope document and chosen pilot condition

Weeks 3–4

Architecture & data design

Architecture diagram and data source plan

Weeks 5–6

Data pipeline

Working pipeline on sample data

Weeks 7–9

Core modeling

Prototype model producing individual-level output

Weeks 10–11

Intervention simulation

Demo of a “what-if” scenario

Week 12

Privacy & extensibility check

Design memo on privacy and extending to a second condition

Weeks 13–14

Integration & testing

Stable end-to-end prototype

Weeks 15–16

Final demo

Final demo, report/poster, code handoff

Team & Partners

The people behind the project

JS

Dr. Janet Shufor Bih

PhD, Learning Sciences · Intelligent Futures, sponsor lead

SC

Sine Charles, DBA

CEO, Advanced Genomics Research Inc. / Antifragility Health Clinic

NK

Noel Kuriakos

Curriculum Designer (Math, Physics, CS/ICT & AI)

VT

Virginia Tech Student Team

Project-based learning capstone team

Resources

Learn more about the project

PDF

Project brief

A one-page summary of the Health Digital Twin: the challenge, the plan and how to get involved.

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Healthcare Education research

See how this project connects to our wider research on healthcare education and emerging technology.

Latest Updates

News from the Health Digital Twin

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Get Involved

Interested in health data, clinical partnerships or piloting the platform? Get in touch.