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6.S897/HST.956 Machine Learning for Healthcare (Spring 2019, MIT OCW). Instructors: Prof. David Sontag and Prof. Peter Szolovits.
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Description
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. (from ocw.mit.edu)
Course content
- Lecture 01 – What Makes Healthcare Unique? Unlimited
- Lecture 02 – Overview of Clinical Care Unlimited
- Lecture 03 – Deep Dive into Clinical Data Unlimited
- Lecture 04 – Risk Stratification, Part 1 Unlimited
- Lecture 05 – Risk Stratification, Part 2 Unlimited
- Lecture 06 – Physiological Time-Series Unlimited
- Lecture 07 – Natural Language Processing, Part 1 Unlimited
- Lecture 08 – Natural Language Processing, Part 2 Unlimited
- Lecture 09 – Translating Technology into the Clinic Unlimited
- Lecture 10 – Application of Machine Learning to Cardiac Imaging Unlimited
- Lecture 11 – Differential Diagnosis Unlimited
- Lecture 12 – Machine Learning for Pathology Unlimited
- Lecture 13 – Machine Learning for Mammography Unlimited
- Lecture 14 – Causal Inference, Part 1 Unlimited
- Lecture 15 – Causal Inference, Part 2 Unlimited
- Lecture 16 – Reinforcement Learning, Part 1 Unlimited
- Lecture 17 – Reinforcement Learning, Part 2 Unlimited
- Lecture 18 – Disease Progression Modeling and Subtyping, Part 1 Unlimited
- Lecture 19 – Disease Progression Modeling and Subtyping, Part 2 Unlimited
- Lecture 20 – Precision Medicine Unlimited
- Lecture 21 – Automating Clinical Workflows Unlimited
- Lecture 22 – Regulation of Machine Learning/ Artificial Intelligence in the US Unlimited
- Lecture 23 – Fairness Unlimited
- Lecture 24 – Robustness to Dataset Shift Unlimited
- Lecture 25 – Interpretability Unlimited
Instructor
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