Machine Learning for Healthcare (Spring 2019)

Machine Learning for Healthcare (Spring 2019)

Prof. Peter Szolovits and Prof. David Sontag via MIT OpenCourseWare Direct link

1. What Makes Healthcare Unique?

1 of 25

1 of 25

1. What Makes Healthcare Unique?

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Machine Learning for Healthcare (Spring 2019)

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

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