6.871/HST.956: Machine Learning for Healthcare

Instructors: David Sontag, Madhur Nayan
Teaching Assistants: Zeshan Hussain, Intae Moon
Graduate level; Units 4-0-8 (counts as an AUS2, AAGS, and II subject; also a EECS AI TQE)
Time: Tuesdays & Thursdays, 9:30-11:00am EST
Location: 32-141
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: Monday 4:00-5:00pm (Room 26-168), Friday 4:00-5:00pm (Room 36-112)
Recitations (required): 4-270, Friday 3:00-4:00pm EST
Contact staffs: mlhc22mit@gmail.com

Course Description | Schedule | Problem sets | Projects | Late Policy | 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 all slide decks, recitation materials, and more.
Class Date Lecture Materials Assignments
Unit 1: Overview of Clinical Care & Data
1 Tuesday Feb 1
Introduction: What makes healthcare unique? [slides]
Week 1 reading :
2 Thursday Feb 3
Overview of Clinical Care [slides]
3 Tuesday Feb 8
Overview of Clinical Data Science [slides]
Unit 2: ML with Clinical Text, Imaging, Physiological, and Administrative Data
4 Thursday Feb 10
Risk Stratification from Structured Health Data [slides]

5 Tuesday Feb 15
Physiological Time-Series [slides]
6 Thursday Feb 17
Intro to Clinical NLP (Monica Agrawal) [slides]

Tuesday Feb 22
Monday Schedule - No Class

7 Thursday Feb 24
Clinical NLP for oncology & application to AI-driven documentation &
(Guest speakers: Kenneth Kehl, DFCI & Monica Agrawal, MIT)
8 Tuesday Mar 1
Intro to ML for medical imaging: algorithms &
application to mammograpy (Adam Yala) [slides]

9 Thursday Mar 3
ML for pathology (Guest speaker: Andy Beck, MD/PhD (CEO, PathAI))

Unit 3: Causal Inference
10 Tuesday Mar 8
Causal graphs, potential outcomes, covariate adjustment [slides]

11 Thursday Mar 10
Assumptions for causal inference, inverse propensity weighting [slides]
12 Tuesday Mar 15
Policy learning, sensitivity analysis, instrumental variables [slides]

13 Thursday Mar 17
Dataset shift [slides]

Spring Break (Mar 21 - 25)
Unit 4: Methods for ML in Healthcare
14 Tuesday Mar 29
Critical appraisal of applying ML in healthcare [slides]
reading :
15 Thursday Mar 31
Weak supervision & learning from noisy labels [slides]

16 Tuesday Apr 5
Survival modeling [slides]
17 Thursday Apr 7
Fairness (Irene Chen) [slides]
18 Tuesday Apr 12
Interpretability [slides]

19 Thursday Apr 14
Human-AI interaction (Hussein Mozannar) [slides]

20 Tuesday Apr 19
Disease subtyping & progression modeling [slides]

21 Thursday Apr 21
Privacy [slides] + Guest speaker: Fei Wang, Weill Cornell Medicine [slides]

Unit 5: Off-policy reinforcement learning & Regulation
22 Tuesday Apr 26
Intro to off-policy reinforcement learning and dynamic treatment regimes [slides]

23 Thursday Apr 28
Guest lecture: Susan Murphy (Harvard)

24 Tuesday May 3
Regulation of AI in healthcare

Project poster session & final exam review
25 Thursday May 5
Project poster session, 9:30am-12pm at the Grier room (34-401)

26 Tuesday May 10
Final exam review

Problem sets

Pset 0 : in order to access sensitive healthcare datasets, you will need to complete several preliminary tasks, including the CITI training and filling out the Data Use Agreement of MIMIC-III dataset. Please upload the full CITI completion report to Canvas once you complete the requested tasks.
Pset 1
Pset 2
Pset 3
Pset 4
Pset 5


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

Late Policy

(starting for pset1 onwards)


Prior years of this course