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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 15 - Causal Inference, Part 2

Instructor: Prof. David Sontag. Prof. Sontag discusses a causal inference setting from a causal graphs perspective.

Go to the Course Home or watch other lectures:

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