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Course6.S897 / HST.95625 lectures31 h 14 min

Machine Learning for Healthcare

Prof. Peter Szolovits and Prof. David Sontag · Massachusetts Institute of Technology

Start with lecture 1 At MIT OpenCourseWare

About

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.

Lectures

Lecture Videos

  1. Lecture 1: What Makes Healthcare Unique? 1 h 11 min · Video
  2. Lecture 2: Overview of Clinical Care 1 h 21 min · Video
  3. Lecture 3: Deep Dive Into Clinical Data 1 h 24 min · Video
  4. Lecture 4: Risk Stratification, Part 1 1 h 13 min · Video
  5. Lecture 5: Risk Stratification, Part 2 1 h 21 min · Video
  6. Lecture 6: Physiological Time-Series 1 h 21 min · Video
  7. Lecture 7: Natural Language Processing (NLP), Part 1 1 h 16 min · Video
  8. Lecture 8: Natural Language Processing (NLP), Part 2 1 h 24 min · Video
  9. Lecture 9: Translating Technology Into the Clinic 1 h 23 min · Video
  10. Lecture 10: Application of Machine Learning to Cardiac Imaging 1 h 22 min · Video
  11. Lecture 11: Differential Diagnosis 1 h 21 min · Video
  12. Lecture 12: Machine Learning for Pathology 56 min · Video
  13. Lecture 13: Machine Learning for Mammography 42 min · Video
  14. Lecture 14: Causal Inference, Part 1 1 h 19 min · Video
  15. Lecture 15: Causal Inference, Part 2 1 h 3 min · Video
  16. Lecture 16: Reinforcement Learning, Part 1 1 h 18 min · Video
  17. Lecture 17: Reinforcement Learning, Part 2 56 min · Video
  18. Lecture 18: Disease Progression Modeling and Subtyping, Part 1 1 h 22 min · Video
  19. Lecture 19: Disease Progression Modeling and Subtyping, Part 2 1 h 13 min · Video
  20. Lecture 20: Precision Medicine 1 h 25 min · Video
  21. Lecture 21: Automating Clinical Work Flows 1 h 21 min · Video
  22. Lecture 22: Regulation of Machine Learning / Artificial Intelligence in the US 1 h 22 min · Video
  23. Lecture 23: Fairness 1 h 18 min · Video
  24. Lecture 24: Robustness to Dataset Shift 1 h 16 min · Video
  25. Lecture 25: Interpretability 1 h 19 min · Video

Syllabus and materials

At MIT OpenCourseWare, as the course publishes them.

Credits and licence

Institution
Massachusetts Institute of Technology
Department
Electrical Engineering and Computer Science; Health Sciences and Technology
Taught by
Prof. Peter Szolovits, Prof. David Sontag
Term
Spring 2019
Level
Graduate
Language
English
Subjects
Medicine and health, Computer science, Engineering
Source
MIT OpenCourseWare
Licence
CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only. MIT OpenCourseWare's terms
Attribution
Prof. Peter Szolovits, Prof. David Sontag. Machine Learning for Healthcare. Spring 2019. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
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