Computational Medicine (Fall 2023)

Instructor

• Wei Wu - weiwu2@cs.cmu.edu

  • • Computational Biology Department, School of Computer Science, Carnegie Mellon University

    • Office hours:  Wednesdays, 1-2 pm EST via Zoom (see Canvas for link)

Teaching Assistant

• Ju-Chun Huang: juchunh@andrew.cmu.edu 

• Office hours: Mondays, 2-3pm, GHC 7404

• Raehash Shah: raehashs@andrew.cmu.edu

  • Office hours: Tuesdays, 4-5pm, GHC 7404

• Wenduo Cheng: wenduoc@andrew.cmu.edu

  • Office hours: Fridays, 2-3pm, GHC 7404

Lectures

Tu,Th 11:00am-12:20 pm, WEH 5403

Course resources

Canvas and Piazza and this website

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Course Description

CMU 02-718 (12 units) & CMU 02-518 (12 units)

This course examines computational methods that enhance our ability to diagnose, treat, and understand human diseases. Topics will include techniques for learning models from clinical data types, including: genomics, transcriptomics, proteomics, metabolomics, imaging, and electronic medical records. Most of the techniques will involve Machine Learning. The course is organized into modules. The first module will be an introduction to the field of Medicine, and how it differs from the basic sciences, as well as personalized and precision medicine. Subsequent modules will focus specific clinical tasks, including: disease phenotyping, biomarker discovery, predictive modeling, and the design and optimization of medical interventions.  Students will be assessed based on homeworks, quizzes, and a course project.  Students are allowed to work in small teams (2-3 students) for the project. Students are not allowed to work in teams for homeworks and quizzes. 

Pre/Co-requisites

The course is designed for graduate and upper-level undergraduate students from a wide variety of backgrounds.  Students should have some background in Machine Learning, but no prior background in Medicine is required.  Students must also understand and agree to comply with Carnegie Mellon University's policies on academic integrity  (see also here).

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Syllabus

Course Outline (see here for detailed schedule)

1. Introduction to medicine (2 weeks)

   1. Branches of medicine

• • Disease categories

  • The hierarchy of medical evidence (meta reviews; randomized clinical trials, etc)

  • Medical Education

  • The Healthcare Industry

  • Clinical data types

2. Computational challenges (10 weeks)

• Phenotyping

• Biomarker discovery

• Predictive Modeling

• Medical imaging analysis 

• Optimizing and/or selecting interventions

3. Advanced topics (2 weeks)

There is no required textbook.

Course outcomes

Students who complete the course successfully will be able to:

• Explain the core computational challenges in medicine

• Discuss and critique computational methods used to address these challenges

• Explain common clinical data types, and what they are used for

• Use software to analyze clinical data and build predictive models for diseases

Assessments

• Take-home quizzes (40% of your final grade)

  • • Five quizzes will be given online via canvas. Students must complete the quizzes alone. 

    • See Canvas for due dates.

  • Homeworks (20% of your final grade)

    • Five homeworks will be given online via canvas. Students must complete the homeworks alone. 

    • See Canvas for due dates.

• Course Project (40% of your final grade)

  • • Students are allowed to work in small teams (2-3 students), or individually.

    • Students/Teams will submit a project proposal mid-semester (see Canvas for due date).

    • Students/Teams will submit a final report detailing their project and give an oral presentation.