6.S897/HST.S53: Machine Learning for Healthcare

Instructor: Professor David Sontag (OH: TBD)
Teaching Assistant: Maggie Makar (OH: Wednesdays 5-7. Location 56-162)
Graduate level; Units 2-0-4 (counts as an AAGS subject)
Time: Wednesdays 2:30-4pm
Location: 56-154
Contact: 6.s897hst.s53[at]gmail.com or through Piazza

Course Description | Schedule | Piazza | Problem sets | Projects | Grading

Course description

Explores machine learning methods for clinical and healthcare applications. Covers concepts of algorithmic fairness, interpretability, and causality. Discusses application of time-series analysis, graphical models, deep learning and transfer learning methods to solving problems in healthcare. Considers how newly emerging machine learning techniques will shape healthcare policy and personalized medicine.


Schedule is tentative and subject to change

Feb 8, 2017

Lecture 1: What makes healthcare unique?

Feb 15, 2017

Lecture 2: Supervised learning for risk stratification

Feb 22, 2017

Lecture 3: Predicting the outcome of interventions: causal inference from observational data

March 1, 2017

Lecture 4: Ethics of machine learning to prioritize care: bias, fairness and accountability

March 8, 2017

Lecture 5: Machine learning in radiology (Guest lecture. Amir Tahmasebi and Christine Swisher, Philips Healthcare)

March 15, 2017

Lecture 6: AI for computational pathology (Guest lecture: Andrew Beck, MD, PhD, BIDMC and PathAI)

March 22, 2017

Lecture 7: Physiological and laboratory time-series

March 29, 2017

No lecture. Spring break.

April 5, 2017

Lecture 8: Preventing high-cost care (Guest lecture: Nigam Shah, MBBS, PhD, Stanford School of Medicine)

April 12, 2017

Lecture 9: Clinical text & natural language processing

April 19, 2017

Lecture 10: Interpretability of machine learning models

April 26, 2017

Lecture 11: Clustering for subtype discovery

May 3, 2017

Lecture 12: Disease progression modeling

May 10, 2017

Lecture 13: Finding optimal treatment policies

May 17, 2017

Project presentations

Problem sets