6.871/HST.956: Machine Learning for Healthcare

Instructors: David Sontag, Peter Szolovits
Teaching Assistants: Willie Boag, Ray Liao, Wei-Hung Weng
Graduate level; Units 4-0-8 (counts as an AUS2, AAGS, and II subject)
Time: Tuesdays & Thursdays, 2:30-4pm EST
Location: Virtual (Zoom meeting via Canvas, and the MIT Touchstone authentication will be required)
Prerequisite: 6.036/6.862 or 6.867 or 9.520/6.860 or 6.806/6.864 or 6.438 or 6.034
Office Hours: Thursday 1-2pm EST, Friday 10:30-11:30am EST
Recitations (optional): 12pm-1pm EST
Contact staffs: mlhc21mit@gmail.com
Canvas page: Canvas Page

Course Description | Schedule | Problem sets | Projects | Late Policy | Grading | Prior Years


Course description

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. Topics include causality, interpretability, algorithmic fairness, time-series analysis, graphical models, deep learning and transfer learning. Guest lectures by clinicians from the Boston area and course projects with real clinical data emphasize subtleties of working with clinical data and translating machine learning into clinical practice.


Please visit Canvas page to access slide decks, reading materials, and more information.

Class Date Lecture Materials Assignments
Unit 1: Overview of Clinical Care & Data
1 Tuesday Feb 16
Introduction: What makes healthcare unique?
  • Prerequisite quiz due
  • Pset 0 out
2 Thursday Feb 18
Overview of Clinical Care
3 Tuesday Feb 23
Deep Dive into Clinical Data
Unit 2: ML for Risk Stratification & Diagnosis
4 Thursday Feb 25
Differential Diagnosis
5 Tuesday Mar 2
Risk Stratification
6 Thursday Mar 4
Learning with Noisy and Censored Labels (survival modeling)
Tuesday Mar 9
Monday Schedule - No Class
7 Thursday Mar 11
Guest Lecture: Prof. Jenna Wiens (UMich)
8 Tuesday Mar 16
Detecting & Mitigating Dataset Shift
Unit 3: ML with Clinical Text, Imaging, & Physiological Data
9 Thursday Mar 18
Physiological Time-Series
Tuesday Mar 23
Class Holiday - No Class
10 Thursday Mar 25
Clinical NLP Part 1
11 Tuesday Mar 30
Clinical NLP Part 2
12 Thursday Apr 1
Unit 4: Human Factors, Integration, & Deployment
13 Tuesday Apr 6
Cooperative Decision Making
14 Thursday Apr 8
15 Tuesday Apr 13
16 Thursday Apr 15
Guest Lectures: Regulation (FDA)
Tuesday Apr 20
Class Holiday - No Class
17 Thursday Apr 22
Guest Lectures: Deployment (Viz.ai)
Unit 5: Causal Inference & Reinforcement Learning
18 Tuesday Apr 27
Causal inference: potential outcomes & covariate adjustment
19 Thursday Apr 29
Causal inference: matching, propensity scores
20 Tuesday May 4
Guest lecture (Noa Dagan and Noam Barda, Clalit Health Services)
21 Thursday May 6
Dynamic treatment regimes & off-policy reinforcement learning
Unit 6: Understanding Disease and its Progression
22 Tuesday May 11
Precision Medicine
23 Thursday May 13
Disease Progression Modeling
Final Presentations
24 Tuesday May 18
Presentations #1
25 Thursday May 20
Presentations #2
Monday May 24
Final Exam

Problem sets

We have five problem sets this year. Please visit Canvas page for more details.


We will release the project guidelines and detailed information in Canvas.

Collaboration Policy

Students must write up their problem sets individually. Students should not share their code or solutions (i.e., the write up) with anyone inside or outside of the class, nor should it be posted publicly to GitHub or any other website. You are asked on problem sets to identify your collaborators. If you did not discuss the problem set with anyone, you should write "Collaborators: none." If in writing up your solution you make use of any external reference (e.g. a paper, Wikipedia, a website), both acknowledge your source and write up the solution in your own words. It is a violation of this policy to submit a problem solution that you cannot orally explain to a member of the course staff.

Plagiarism and other dishonest behavior cannot be tolerated in any academic environment that prides itself on individual accomplishment. If you have any questions about the collaboration policy, or if you feel that you may have violated the policy, please talk to one of the course staff.

Late Policy

(starting for pset1 onwards)


Grade breakdown

Prior years of this course