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

Announcements


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

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: Medical image analysis I (Guest lecture. Amir Tahmasebi and Christine Swisher, Philips Healthcare)


March 15, 2017

Lecture 6: Medical image analysis II (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: Clinical natural language processing

  • [Slides] Lecture 8


April 12, 2017

Lecture 9: Interpretability of machine learning models

  • [Slides] Lecture 9


April 19, 2017

Lecture 10: Clustering for subtype discovery

  • [Slides] Lecture 10


April 26, 2017

Lecture 11: Probabilistic modeling of disease progression

  • [Slides] Lecture 11

May 3, 2017

Lecture 12: Clinical decision support: expert systems, utility theory

  • [Slides] Lecture 12


May 10, 2017

Lecture 13: Finding optimal treatment policies: Markov decision processes

  • [Slides] Lecture 13


May 17, 2017

Project presentations

Problem sets


Grading


Projects