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Dr. Ram KumarDiabetes & metabolic health

Medical AI · Track 10 of 10

Applied case studies: diabetes & metabolic health

End-to-end projects on problems from diabetes care, bringing together the methods from every earlier track.

Capstone projects. Each lists the earlier lessons it builds on.

By the end of this track you should be able to

  • Forecast glucose from continuous glucose monitoring (CGM) data
  • Build and evaluate a diabetic retinopathy screening model
  • Develop a diabetic kidney disease risk model
  • Build a RAG assistant over a diabetes guideline that cites its sources

Lessons

  1. Forecasting glucose from CGM data

    Intermediate · Technical · 35 min · Exercise

  2. Diabetic retinopathy screening

    Intermediate · Clinical · 30 min · Exercise

  3. Diabetic kidney disease risk modelling

    Intermediate · Clinical · 30 min · Exercise

  4. Diabetic foot-ulcer image classification

    Intermediate · Clinical · 30 min · Exercise

  5. A RAG assistant over a diabetes guideline

    Advanced · Technical · 35 min · Exercise

Educational content only. This material teaches methods; it is not clinical advice. Code, models and examples here are not validated medical devices and must not be used to make decisions about real patients without proper clinical validation, governance and any required regulatory approval. Examples use synthetic or openly licensed de-identified data only. Never paste identifiable patient information into public AI tools. Mentions of models, tools or vendors are for identification, not endorsement.