6.7930[6.871]/HST.956: Machine Learning for Healthcare

Instructor: Peter Szolovits
Teaching Assistants: Helen Bang, Shibal Ibrahim, Subha Nawer Pushpita
Graduate level; Units 4-0-8 (counts as an AUS2, AAGS, and II subject; also a EECS AI TQE)
Lectures: Tuesdays & Thursdays, 2:30-4:00pm Eastern Time, 54-100
Recitations (required): Friday, 3:00-4:00pm, 4-270
Prerequisite: 6.3900[6.036] or 6.7900[6.867] or 9.520/6.7910[6.860] or 6.8611/6.8610[6.806/6.864] or 6.4102/6.4100[6.438/6.034] or equivalent machine learning class. (Subscripted bracketed numbers are the class numbers before the recent mass renumbering of all EECS classes.)
Office Hours, most weeks:
When
Where
Who
Mon 4-5
 36-112
Shibal
Tue 1:30-2:30
38-166
Pushpita
Wed 4:30-5:30
34-301
Helen

Contact staff: mlhc2024@googlegroups.com

Announcements


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.


Schedule

The schedule for classes is under significant revision throughout the semester.

Please visit our Canvas site to access all slide decks, recitation materials, and more.



Class Date Lecture & Materials Assignments
Overview of Clinical
Care & Data
1 Tue, Feb 6
Introduction: What makes healthcare unique?
Reading:


2
Thu, Feb 8
Overview of Clinical Care
Reading:
PS 0 out
3
Tue, Feb 13
Overview of Clinical Data Science
Reading:
PS0 due
ML with Clinical Text,
Imaging, Physiological,
and Administrative Data
4
Thu, Feb 15
Risk Stratification from Structured Health Data
Reading:
ps1 out

Tue, Feb 20
No class -- Monday schedule of classes  
5 Thu, Feb 22
Cautionary Tales; Discussion of Project Ideas; Guest speaker Dr. Leo Anthony Celi, BIDMC & IMES-LCP
Description of possible class projects; Dr. Celi and Dr. Alexander Dale, MIT Solve Director of Global Programs.
Reading:

6 Tue, Feb 27
Risk Stratification as Regression; and
Physiological Time-Series
Reading:
see potential project list
7
Thu, Feb 29
Intro to Clinical NLP
Reading:
Beam AL, Kompa B, Schmaltz A, Fried I, Weber G, Palmer N, et al. Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data. In: Biocomputing 2020, p. 295-306.
ps1 due
ps2 out
8
Tue, Mar 5
Contemporary Clinical NLP Methods [Pushpita]
Reading:
Helpful optional readings:
fill our project preference form
9
Thu, Mar 7
Survival Analysis, Censoring, Proportional Hazard Models
Reading:

Causal Inference
10
Tue, Mar 12
Causal Inference, Conditional Treatment Effects
Reading for lectures 10 and 11 (feedback form to be submitted after lecture 11):

11
Thu, Mar 14
Causal Inference, continued, and Intro to Reinforcement Learning
ps2 due
ps3 out
12
Tue, Mar 19
Causal Inference, continued; policies, instrumental variables, bias from unmeasured confounders
No additional reading

13
Thu, Mar 21
Dataset Shift
Reading:
(Class meets in 10-250!)

Real World Deployment Challenges

Mar 25-29
Spring Break -- No classes

14
Tue, Apr 2
Learning with Noisy Labels, Weak Supervision & In-context learning [Pushpita]
Reading:
ps3 due
15
Thu, Apr 4 The future of EHRs; guest lecture by Dr. Monica Agrawal
Reading:
ps4 out
16
Tue, Apr 9
ML for Drug Discovery and Repurposing; Guest lecture by Prof. Jim Collins
Reading:
Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A Deep Learning Approach to Antibiotic Discovery. Cell. 2020 Feb;180(4):688-702.e13.

17
Thu, Apr 11
Human-AI Collaboration in Clinical ML; guest lecture by Hussein Mozannar
Reading:

18
Tue, Apr 16
Interpretability [Shibal and Helen]
Reading:
ps4 due
19
Thu, Apr 18
ML for Medical Imaging: algorithms & applications; guest lecture by Dr. Alexander Goehler
Readings:

20
Tue, Apr 23
Genomics in Medicine: practices and ethics; guest lecture by Sarah Faye Gurev, Courtney Shearer, and Rose Orenbuch
Reading:

21
Thu, Apr 25
Methods for genomics in medicine; guest lecture by Sarah Faye Gurev, Courtney Shearer, and Rose Orenbuch
Reading:

22
Tue, Apr 30
Fairness; Guest lecture by Prof. Marzyeh Ghassemi
Reading:

23
Thu, May 2
Flipping the clinic with AI: Medicine in the absence of primary care; Guest lecture by Dr. Isaac Kohane, Harvard Dept. of Biomedical Informatics

24
Tue, May 7
Privacy and Confidentiality
Reading:

25
Thu, May 9
Regulation, Law and Deployment; Guest lecture by Christopher Conley, JD, BU Law School
Readings:

26
Tue, May 14
Student Project Presentations -- Grier Room (34-401), using posters


May 20, 9am-noon
Final Exam at Johnson Ice Rink

Reading Assignments

Many of the lectures are associated with related papers that should help you think about the lecture topic. For each reading assignment, you are expected to submit a brief summary of the three most important ideas of the paper, as short bullet points.

Problem sets

The problem sets pdfs will be available here (not that some data for the problem sets is not publicly available): Some of the recitation materials will be available here:

Projects

We are recruiting a group of doctors with interesting clinical problems to mentor teams of students who will work on them. We will form teams and match to problems/mentors a few weeks into the class. We will release the project guidelines and detailed information in Canvas. For now, please look at the list of proposed projects here.

Grading

Late Policy (starting for pset1 onwards)

Scenarios:

Use of Generative AI

Although you may choose to use generative AI tools to help think through problems, any work you turn in is your responsibility. Pedagogically, you will also learn much more from working through any problem than from copying its answer from a tool or other source. We may ask you to explain the rationale for an answer if it appears that it is not original to you.


Prior years of this course