6.871/HST.956: Machine Learning for Healthcare

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.

Prerequisite quiz

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.

Schedule

Schedule is subject to change.

Problem sets

Projects

Lecture Videos

Scribing

Scribing is one mandatory component of your participation grade. Scribing for a lecture will consist of taking high-quality, 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.

Late Policy

(starting for pset1 onwards)

• [2 "slack" days] We understand that sometimes things outside one's control prevent submitting by the deadline. As such, each student is given 2 "slack" days that they can use throughout the semester (e.g. you could submit two psets one day late each or you could submit one pset two days late) without a late penalty. the days do not subdivide into sub-day units: 2 hours late would spend one of the slack days without 22 hours of "rollover". In your pdf writeup, specify how many slack days you are using (they cannot be used retroactively).
• [10% off per unexcused late day.] If you submit a pset 3 days late and use 1 slack day, then this is 2 unexcused late days, which translates to 20% off your homework.
• [Max 4 days late.] Homework will not be accepted after 4 days late, regardless of how many slack days are used, absent communication from S3 or OGE.
• [write on homework] In order to use a slack day, students must include it in writing on their submission pdf. Otherwise, TAs will assume no slack days used and deduct 10% for each late day.

Scenarios:

• Sam uses 2 slack days on HW3. This is the first time Sam has used any slack days. Sam now has 0 remaining slack days and receives her homework score with no penalty.
• Jamie uses 1 slack day on HW3 but submits 52 hours after the deadline. Therefore Jamie is 3 days late (rounded up) and receives 20% off the graded homework. This is the first time Jamie has used any slack days, so Jamie now has 1 slack day remaining.
• Cory has never used any slack days, but submits HW5 100 hours late with no note / communication from S3 or OGE. This is more than 4 days after the deadline, so the work is not accepted, and Cory receives a zero.

Prior years of this course