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.

Schedule

Please visit Canvas page to access slide decks, reading materials, and more information.

Problem sets

Projects

Collaboration Policy

Students must write up their problem sets individually. Students should not share their code or solutions (i.e., the write up) with anyone inside or outside of the class, nor should it be posted publicly to GitHub or any other website. You are asked on problem sets to identify your collaborators. If you did not discuss the problem set with anyone, you should write "Collaborators: none." If in writing up your solution you make use of any external reference (e.g. a paper, Wikipedia, a website), both acknowledge your source and write up the solution in your own words. It is a violation of this policy to submit a problem solution that you cannot orally explain to a member of the course staff.

Plagiarism and other dishonest behavior cannot be tolerated in any academic environment that prides itself on individual accomplishment. If you have any questions about the collaboration policy, or if you feel that you may have violated the policy, please talk to one of the course staff.

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.

Grade breakdown

• 40% homework
• 40% course project
• 15% final exam
• 5% participation (class attendance required)

Prior years of this course