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6.S897 Machine Learning for Healthcare

6.S897/HST.956 Machine Learning for Healthcare (Spring 2019, MIT OCW). Instructors: Prof. David Sontag and Prof. Peter Szolovits. 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)

Lecture 13 - Machine Learning for Mammography

Dr. Adam Yala discusses deep learning models for mammogram interpretation and triaging. While many aspects of cancer detection based on mammograms are similar to those of natural image classification (ImageNet), there are some key differences.


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Lecture 01 - What Makes Healthcare Unique?
Lecture 02 - Overview of Clinical Care
Lecture 03 - Deep Dive into Clinical Data
Lecture 04 - Risk Stratification, Part 1
Lecture 05 - Risk Stratification, Part 2
Lecture 06 - Physiological Time-Series
Lecture 07 - Natural Language Processing, Part 1
Lecture 08 - Natural Language Processing, Part 2
Lecture 09 - Translating Technology into the Clinic
Lecture 10 - Application of Machine Learning to Cardiac Imaging
Lecture 11 - Differential Diagnosis
Lecture 12 - Machine Learning for Pathology
Lecture 13 - Machine Learning for Mammography
Lecture 14 - Causal Inference, Part 1
Lecture 15 - Causal Inference, Part 2
Lecture 16 - Reinforcement Learning, Part 1
Lecture 17 - Reinforcement Learning, Part 2
Lecture 18 - Disease Progression Modeling and Subtyping, Part 1
Lecture 19 - Disease Progression Modeling and Subtyping, Part 2
Lecture 20 - Precision Medicine
Lecture 21 - Automating Clinical Workflows
Lecture 22 - Regulation of Machine Learning/ Artificial Intelligence in the US
Lecture 23 - Fairness
Lecture 24 - Robustness to Dataset Shift
Lecture 25 - Interpretability