Educational Activity

    1. Self assessment questions

    1. Natural Language Processing (With a classical hematology use case)

    1. Post activity questions

About this course

  • Free
  • 3 lessons
  • 0 hours of video content

Learning Objectives

Upon successful completion of this activity, you should be better prepared to:

  • Define Natural Language Processing (NLP) and recognize its role in tasks like identifying conditions such as Venous Thromboembolism (VTE) in clinical text

  • Outline the evolution of NLP, from early rule-based systems to modern Large Language Models

  • Describe core techniques for how computers process text, including vectorization and the attention mechanism in transformer models

  • Explain how Large Language Models can be used for specific tasks through prompt engineering rather than traditional model customization

Targeted Audience

  • Healthcare professionals

    This educational activity has a primary target audience of oncologists, hematologists, bone marrow transplant experts, researchers, machine learning and data scientists focusing on the application of AI in the field of oncology and bone marrow transplantation.

  • AI Enthusiast

    Anyone interested in the application of AI in healthcare and specifically bone marrow transplantation.

Presenter

Barbara Lam, MD

Dr. Barbara Lam is a board-certified hematologist and oncologist and clinician-researcher focusing on classical hematology and clinical informatics. A graduate of the Keck School of Medicine of the University of Southern California, Dr. Lam completed a residency in internal medicine at Beth Israel Deaconess Medical Center, Harvard Medical School, where she was also a fellow in hematology-oncology and clinical informatics. Dr. Lam’s research focuses on the intersection of hematology, oncology and clinical informatics, with a particular emphasis on how technology can best serve patients and providers.