Skip to content
View Yeongseo-Lee's full-sized avatar

Block or report Yeongseo-Lee

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
Yeongseo-Lee/README.md

Hi, I'm Yeongseo Lee

I am an incoming PhD student in Nursing interested in digital health, healthcare AI, behavior change, and adaptive intervention design.

My current portfolio explores how walking behavior can be tracked, analyzed, translated into personalized micro-interventions, evaluated for safety and usability, and optimized through adaptive prompt timing.

Digital Health AI Portfolio

Track → Analyze → Design → Evaluate → Optimize

1. Walking Behavior Pattern Tracker

A React-based digital health prototype for logging and visualizing walking behavior patterns by purpose and barriers.

2. Walking Adherence Prediction

A Python data science prototype using simulated contextual data to explore predictors of daily walking adherence.

3. AI-Assisted Behavior Intervention Designer

A context-aware digital health prototype that translates walking barriers into feasible micro-intervention suggestions using transparent recommendation logic.

4. AI Health Suggestion Evaluation

A responsible AI evaluation prototype assessing simulated health behavior suggestions for safety, feasibility, usability, personalization, and clinical appropriateness.

5. Adaptive Micro-Action Trigger Engine

A context-aware timing optimization prototype that adapts micro-action prompts based on user feedback, health constraints, and response patterns.

Research Interests

  • Digital health
  • Healthcare AI
  • Behavior change
  • Chronic disease self-management
  • Walking interventions
  • Adaptive interventions
  • Just-in-time adaptive interventions
  • Responsible AI in health
  • Human-centered health technology
  • Implementation science

Current Direction

I am interested in developing context-aware adaptive algorithms that help people initiate small, feasible health behaviors at the right moment.

My long-term goal is to design and evaluate digital health systems that translate behavioral data into safe, usable, and personalized interventions for chronic disease self-management.

Popular repositories Loading

  1. 01.walking-behavior-tracker 01.walking-behavior-tracker Public

    A simple digital health prototype for tracking walking behavior patterns.

    JavaScript

  2. 02.walking-adherence-prediction 02.walking-adherence-prediction Public

    A digital health data science prototype exploring how contextual signals may predict walking adherence using simulated data.

    Python

  3. 03.AI-behavior-intervention-designer 03.AI-behavior-intervention-designer Public

    A digital health prototype for AI-assisted behavior intervention design using transparent, context-aware recommendation logic.

    JavaScript

  4. 04.AI-health-suggestion-evaluation 04.AI-health-suggestion-evaluation Public

    A responsible AI evaluation prototype assessing simulated health behavior suggestions for safety, feasibility, usability, and personalization.

    Python

  5. 05.adaptive-micro-action-trigger-engine 05.adaptive-micro-action-trigger-engine Public

    A context-aware timing optimization prototype that adapts micro-action prompts based on user feedback, health constraints, and response patterns.

    JavaScript

  6. Yeongseo-Lee Yeongseo-Lee Public