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Massachusetts Institute of Technology

Machine Learning for Healthcare

Massachusetts Institute of Technology via MIT OpenCourseWare

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Overview

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.

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

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Reviews

4.5 rating, based on 6 Class Central reviews

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  • Profile image for Yash Hadge
    Yash Hadge
    I recently completed the Machine Learning for Healthcare course, and I must say it has been a transformative experience. The course not only provided a comprehensive overview of the fundamental principles of machine learning but also demonstrated it…
  • Pallavi Jha
    Impressive course! Comprehensive journey through ML's impact on healthcare, from clinical data analysis to AI ethics.
  • Hassan Waqar
    Excellent free course! It exceeded my expectations. The content is highly relevant and presented in a well-structured manner, making complex concepts easy to grasp. The instructors are knowledgeable, engaging, and always ready to provide support. I…
  • Profile image for Hemant Sudarshan
    Hemant Sudarshan
    Delving into the nexus of healthcare and advanced machine learning, the Spring 2019 course was both comprehensive and illuminating. It adeptly showcased the transformative capacity of ML in reshaping diagnostics, treatment, and patient well-being. The hands-on assignments and projects were instrumental in fortifying pragmatic comprehension, facilitating adeptness in employing ML within medical contexts. The instructors' profound expertise, coupled with pertinent guest lectures, undeniably enhanced the overall learning expedition. In summation, an invaluable resource for those aiming to harness ML progress within the realm of healthcare.
  • Adebeye Oluwaseun
    Good and helpful.
    It is highly inspiring , it will help me a lot to do more and also love the learning techniques.
    I will recommend it to all my friends and colleagues to also come and learn and add more value to there career o has to be better person in life and earn a good living.
  • Reem Mohammed Almubarak
    It contains a material rich in knowledge and I was far from these things and now I got a lot of information
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