Machine Learning for Healthcare
Offered By: Massachusetts Institute of Technology via MIT OpenCourseWare
Course Description
Overview
Syllabus
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
Tags
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