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

• Finally, as of Feb 27, 54-100 is ready for use, so going forward, we will meet in that room.
• One more disruption: On March 21, 54-100 is in use for a dedication event, so we will meet that day (only) in 10-250.
  

Announcements

• Lectures, lecture slides, readings, recitation material, and assignments will be posted to our  Canvas site
• We expect you to attend all lectures and recitations, and will do a virtual roll call for each using the following links:
• Lecture attendance submission
• Recitation attendance submission
  
• When readings are posted for a lecture, we expect you to provide us a brief summary of that reading by the day after the corresponding lecture. Three relevant bullet points are sufficient. Please submit these at the following link:
• Reading summary submission
  
• Please accept your email invitation or add yourself to the course Piazza (find the link in a Canvas announcement)
• We do not plan to support remote participation in lectures and our expectation is that students will attend class in person unless an exception is granted (e.g., students needing to quarantine/isolate, or a clinician who is on service).
• See last year's site for the 2023 schedule and problem sets; this year's will be similar, but not identical. See that site for slides or MIT OpenCourseWare  for videos of past lectures.
• This class will have a final exam, but no midterm.
• Class group projects will be organized with clinician mentors. Students will present their results in a poster session during the last week of class.

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.

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.

• [Problem Set 0]
• [Problem Set 1]
• [Problem Set 2]
• [Problem Set 3]
• [Problem Set 4]

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

• 40% course project
• 30% homework (~4 problem sets; both theory & practice)
• 20% final exam
• 5% participation – note: class attendance is required (with obvious exceptions for illness, etc.)
  
• 5% reading responses

Late Policy (starting for pset1 onwards)

• [4 "slack" days] We understand that sometimes things outside one's control prevent submitting by the deadline. As such, each student is given 4 "slack" days that they can use throughout the semester (e.g. you could submit four psets one day late each or you could submit one pset three days late and another one day 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 2 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.

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.

• 2023
• 2022
• 2021
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• 2017