HST 953 Course Home Page
Welcome to HST 953!
HST.953 Fall 2021
Friday, 9:30 AM - 12:30 PM
E25-117
Instructors: Dr. Marzyeh Ghassemi, Dr. Leo Celi
Course Staff: Dr. Eric Gottlieb, Dr. Ned McCague, Dr. Kennith Paik
TA: Abbas Zeitoun
Overview
HST.953 is a course about the practical considerations for operationalizing machine learning in healthcare settings. We begin the course with a focus on robust, private and fair machine learning (ML) using real retrospective healthcare data. We follow this with experiences in visualization (VIS) that target utility and clinical value. Finally, we explore the intermediate "implementation science" (IMP) tying together how real models might be potentially used through a visual system by practicing clinical staff.
The course will involve three homework assignments (one each on machine learning, visualization and implementation) followed by a course project proposal and presentation.
- All students are required to complete human subjects training and submit proof of access for MIMIC-III and the eICU-CRD databases.
- All students regardless of their enrollment status are expected to join a project group and contribute to a final project.
HST.953 is not intended to teach graduate machine learning or visualization skills to students, and we expect that students will have some working knowledge of both in order to complete homework assignments and the project.
We recommend the following courses, or some equivalent experience with subject matter in ML, visualization and HCI:
Recommended Courses:
CS Grad ML 6.867
CS Grad ML in Health 6.S897 / HST.956
CS Grad Visualization 6.813
Grading
Weekly Reflections/edX: While everyone might benefit from both edX content and weekly readings, you will be asked at the beginning of the semester to choose between completing weekly reflections or completing the Collaborative Data Science for Healthcare edX course.
- The weekly reflections, corresponding to Week 2 - Week 10, will be done as a Canvas discussion, are due before class, and are worth 1 point (1.67% of your grade) per week. This means that reflections are worth a total of 15% of your grade.
- Alternatively, you may choose to complete the Collaborative Data Science for Healthcare course throughout the semester to earn that 15% of your grade. The course may be accessed here: https://www.edx.org/course/collaborative-data-science-for-healthcare Links to an external site.
Three Problem Sets: Problem sets 1, 2, and 3 are each work 10 points, or 16.67% of your grade. This means problem sets are worth a total of 50% of your grade.
Course Final Project: The submission of the project teams is worth 1 point (1.67% of your grade), the final project presentation is worth 10 points (16.67% of your grade) and the final project write up is worth 10 points (16.67% of your grade).
Plagiarism: Student code submissions may be submitted by the instructors to a plagiarism detection tool for a review of similarity and detection of possible plagiarism. Submissions will be used solely for the purpose of detecting similarity, and are not retained indefinitely on the server; typically results are deleted after 14 days but may be removed sooner. For more information on the tool used, refer to https://theory.stanford.edu/~aiken/moss/.
Extra Credit
Scribing: We have a weekly extra credit of 1 point for Latexing/scribing the lectures for the week, and submitting them to the class for review. A maximum of 5 extra credit points can be earned per student.
Schedule
Project Details
Projects and Authorship
A note on collaboration: Research is a collaborative activity and we encourage all students to collaborate and learn from each other. In general, when you put your name on something for research, you must: a) have materially contributed to the work, b) be able to defend the
research, and c) acknowledge the contribution of others. Keep this in mind when working together and submitting material for evaluation.
A note on authorship: As noted, the expectation is that by the end of the course the final project will be sufficiently developed to submit to a peer-reviewed journal. The author order can be a somewhat controversial issue and is left to the project participants to decide. We would strongly encourage you to discuss what the order will be, or what philosophy you will use to decide the order while forming groups. In the case of a dispute during or after the course, the instructors will likely not be able to mediate in any meaningful way. We would also recommend equal authorship (now more common), but the decision is left to each team.
For the clinicians: If you expect a certain level of authorship (first, last, etc.) you should mention this in your project pitch. Keep in mind that this is a two-way street involving both clinicians and data scientists. If a project fails to garner enough interest, it may not be able to be completed as part of the course.
A note on acknowledgement: Papers that result from work done during this course should recognize the contributions of the course in an acknowledgement or in other sections. The suggested language is: "This manuscript was composed by participants in the HST.953 course at the Massachusetts Institute of Technology, Fall 2021.'"
Project Descriptions
- Intravenous Insulin Infusion, DKA, Hypoglycemia, and Machine Learning - Zach Taxin
- Identifying Acid-Base Status in Patients in the ICU - Nathan Raines and Marcus Foo
- Mapping Serum Lactate to Outcomes Across Race-Ethnicities - Brian Bustos
- Simulation of Time-Limited Trials for Critically-Ill Patients on Mechanical Ventilation - Anthony O’brien
- Relative Hypoglycemia and Mortality in Critically-Ill Patients - Sreekar Mantena
- Carbon Dioxide Gap in the ICU: How Can We Evaluate Tissue Perfusion at the Bedside? - Asher Mendelson
- AKI Trajectory Across Marginalized ICU Populations - Eric Gottlieb and Periklis Kyriazis
- Trends in Sepsis Survival - Traci King and Sunil Nair
- Two's a Party, Three's a Crowd: The Three Presser Problem - Emmett Kistler, Gloria Hyunjung and Leo Celi
- Predicting COPD from Chest X-rays - Patrick Seastedt
Clinical Collaborators
Data Science Mentorship
We realize that there are many backgrounds engaging with this course, and have a volunteer group of 15 data science volunteers who will be assigned to sets of 2-3 students and can assist with homework and project questions. You will receive your mentor assignment the second week of class.