Catalog

Machine Learning for Healthcare

Electrical Engineering and Computer Science MIT CC BY-NC-SA 4.0 25 lectures

This course introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows.

Syllabus

  1. 1 Lecture 1: What Makes Healthcare Unique?
  2. 2 Lecture 2: Overview of Clinical Care
  3. 3 Lecture 3: Deep Dive Into Clinical Data
  4. 4 Lecture 4: Risk Stratification, Part 1
  5. 5 Lecture 5: Risk Stratification, Part 2
  6. 6 Lecture 6: Physiological Time-Series
  7. 7 Lecture 7: Natural Language Processing (NLP), Part 1
  8. 8 Lecture 8: Natural Language Processing (NLP), Part 2
  9. 9 Lecture 9: Translating Technology Into the Clinic
  10. 10 Lecture 10: Application of Machine Learning to Cardiac Imaging
  11. 11 Lecture 11: Differential Diagnosis
  12. 12 Lecture 12: Machine Learning for Pathology
  13. 13 Lecture 13: Machine Learning for Mammography
  14. 14 Lecture 14: Causal Inference, Part 1
  15. 15 Lecture 15: Causal Inference, Part 2
  16. 16 Lecture 16: Reinforcement Learning, Part 1
  17. 17 Lecture 17: Reinforcement Learning, Part 2
  18. 18 Lecture 18: Disease Progression Modeling and Subtyping, Part 1
  19. 19 Lecture 19: Disease Progression Modeling and Subtyping, Part 2
  20. 20 Lecture 20: Precision Medicine
  21. 21 Lecture 21: Automating Clinical Work Flows
  22. 22 Lecture 22: Regulation of Machine Learning / Artificial Intelligence in the US
  23. 23 Lecture 23: Fairness
  24. 24 Lecture 24: Robustness to Dataset Shift
  25. 25 Lecture 25: Interpretability

Course materials