MIT 6.S897 Machine Learning for Healthcare, Spring 2019 – CareerPhi

Instructors: David Sontag, Peter Szolovits View the complete course: https://ocw.mit.edu/6-S897S19 Introduces students to machine learning in healthcare, 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. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu

Course Content

2. Overview of Clinical Care

16. Reinforcement Learning, Part 1

15. Causal Inference, Part 2

14. Causal Inference, Part 1

13. Machine Learning for Mammography

12. Machine Learning for Pathology

11. Differential Diagnosis

10. Application of Machine Learning to Cardiac Imaging

8. Natural Language Processing (NLP), Part 2

7. Natural Language Processing (NLP), Part 1

6. Physiological Time-Series

5. Risk Stratification, Part 2

4. Risk Stratification, Part 1

3. Deep Dive Into Clinical Data

1. What Makes Healthcare Unique?

9. Translating Technology Into the Clinic

25. Interpretability

24. Robustness to Dataset Shift

22. Regulation of Machine Learning / Artificial Intelligence in the US

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About Instructor

Sonu

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