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Uedu Open / Machine Learning for Healthcare
6.S897

Machine Learning for Healthcare

Prof. Peter Szolovits, Prof. David Sontag | Spring 2019
Data Science, Analytics & Computer Technology AI User Experience Machine Learning Computer Science Engineering Health & Medicine Imaging
前往原始課程
CC BY-NC-SA 4.0
課程簡介
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.
課程資訊
來源MIT 開放式課程
科系Electrical Engineering and Computer Science
語言English
影片數25
課程影片 (25)
1
1. What Makes Healthcare Unique?
1. What Makes Healthcare Unique?
2
2. Overview of Clinical Care
2. Overview of Clinical Care
3
3. Deep Dive Into Clinical Data
3. Deep Dive Into Clinical Data
4
4. Risk Stratification, Part 1
4. Risk Stratification, Part 1
5
5. Risk Stratification, Part 2
5. Risk Stratification, Part 2
6
6. Physiological Time-Series
6. Physiological Time-Series
7
7. Natural Language Processing (NLP), Part 1
7. Natural Language Processing (NLP), Part 1
8
8. Natural Language Processing (NLP), Part 2
8. Natural Language Processing (NLP), Part 2
9
9. Translating Technology Into the Clinic
9. Translating Technology Into the Clinic
10
10. Application of Machine Learning to Cardiac Imaging
10. Application of Machine Learning to Cardiac Imaging
11
11. Differential Diagnosis
11. Differential Diagnosis
12
12. Machine Learning for Pathology
12. Machine Learning for Pathology
13
13. Machine Learning for Mammography
13. Machine Learning for Mammography
14
14. Causal Inference, Part 1
14. Causal Inference, Part 1
15
15. Causal Inference, Part 2
15. Causal Inference, Part 2
16
16. Reinforcement Learning, Part 1
16. Reinforcement Learning, Part 1
17
17. Reinforcement Learning, Part 2
17. Reinforcement Learning, Part 2
18
18. Disease Progression Modeling and Subtyping, Part 1
18. Disease Progression Modeling and Subtyping, Part 1
19
19. Disease Progression Modeling and Subtyping, Part 2
19. Disease Progression Modeling and Subtyping, Part 2
20
20. Precision Medicine
20. Precision Medicine
21
21. Automating Clinical Work Flows
21. Automating Clinical Work Flows
22
22. Regulation of Machine Learning / Artificial Intelligence in the US
22. Regulation of Machine Learning / Artificial Intelligence in the US
23
23. Fairness
23. Fairness
24
24. Robustness to Dataset Shift
24. Robustness to Dataset Shift
25
25. Interpretability
25. Interpretability