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, timeseries 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.
This quiz will not count toward your grade, but will be used by the course staff to check prerequisites (6.036/6.862 or 6.867 or 9.520/6.860 or 6.806/6.864 or 6.438 or 6.034) and to assess students' preparation for this class.
You can take the prerequisite quiz here. If you intend to take this class, you must take this quiz before 11:59 p.m. EST on Tuesday, February 4th, 2020
Schedule is subject to change.
Class  Date  Lecture Materials  Assignments 

1  Tuesday Feb 04 
Introduction: What makes healthcare unique?


2  Thursday Feb 06 
Overview of Clinical Care


3  Tuesday Feb 11 
Deep Dive into Clinical Data


4  Thursday Feb 13 
Risk Stratification


Friday Feb 14  Recitation on querying MIMIC with SQL to recreate Lecture 3 slides  
Tuesday Feb 18  Monday Schedule  No Class 

5  Thursday Feb 20 
Learning with Noisy and Censored Labels


6  Tuesday Feb 25 
Clinical Natural Language Processing (NLP)


7  Thursday Feb 27 
Interpretability


Friday Feb 28  Recitation primarily on contextual embeddings for medical disambiguation  
8  Tuesday Mar 3 
Learning to Defer & Uncertainty


9  Thursday Mar 5 
Small to big Data: Case Studies with Physiological Timeseries


10  Tuesday Mar 10 
Detecting Dataset Shift


11  Thursday Mar 12 
Fairness


12  Tuesday Mar 17 
Cancelled due to COVID


13  Thursday Mar 19 
Cancelled due to COVID


Tuesday Mar 24  Spring Vacation 

Thursday Mar 26  Spring Vacation 

14  Tuesday Mar 31 
Causal Inference: Potential Outcomes, Regression


15  Thursday Apr 2 
Causal Inference: Inverse Propensity Reweighting


16  Tuesday Apr 7 
Offpolicy Reinforcement Learning


17  Thursday Apr 9 
Guest Lecture: David Bates, Harvard Medical School


18  Tuesday Apr 14  Guest Lecture: Andrew Beck, PathAI


Thursday Apr 16 
No Class  QUIZ


19  Tuesday Apr 21 
Precision Medicine


20  Thursday Apr 23 
Disease Progression & Subtyping Pt. 1


21  Tuesday Apr 28 
Disease Progression & Subtyping Pt. 2


Thursday Apr 30 
Project Discussions


22  Tuesday May 5 
Differential Diagnosis


23  Thursday May 7 
Automating Clinical Workflows


Tuesday May 12  Final Project Discussion and Poster Session 
In order to access sensitive healthcare datasets, you will need to complete several preliminary tasks. Please see here for full instructions and submission details.
Due to the switch to virtual classes, we have links to recent course videos available on Stellar here. These are only accessible to students enrolled in the course.
Scribing is one mandatory component of your participation grade. Scribing for a lecture will consist of taking highquality, detailed notes during the lecture in question then working with your scribing team to compile a polished, electronic version of these notes under our direction.
Full instructions for scribing, including how to signup for particular scribe dates, can be found here. Please let us know via Piazza if you have any questions!
(starting for pset1 onwards)
Scenarios: