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Dr. Ram KumarDiabetes & metabolic health

Medical AI · Track 4 of 10

Clinical NLP

Getting structure out of clinical text: notes, named-entity recognition, coding, embeddings and de-identification.

Bridges classical ML and large language models.

By the end of this track you should be able to

  • Clean and explore clinical notes
  • Extract conditions, medicines and findings with named-entity recognition
  • Represent clinical text with embeddings
  • De-identify free text before it is shared or processed

Lessons

  1. Working with clinical notes

    Coming soon

  2. Named-entity recognition and clinical coding

    Coming soon

  3. Clinical text embeddings

    Coming soon

  4. De-identifying clinical free text

    Coming soon

Lessons in this track are being written and reviewed. In the meantime, start with the Foundations track.

Next track: Generative AI & LLMs

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.