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Massachusetts Institute of Technology

Machine Learning for Healthcare (Spring 2019)

Massachusetts Institute of Technology via MIT OpenCourseWare


Course Features
  • Video lectures
  • Captions/transcript
  • Lecture notes
  • Projects (no examples)
Course Description

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.


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

Taught by

Prof. Peter Szolovits and Prof. David Sontag


3.0 rating, based on 1 Class Central review

Start your review of Machine Learning for Healthcare (Spring 2019)

  • Reem Mohammed Almubarak
    It contains a material rich in knowledge and I was far from these things and now I got a lot of information

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