Fall 2018

Course Director

Christopher James Langmead (Carnegie Mellon University) - my initials at cs dot cmu dot edu
Office hours;  Wednesdays, 1pm-2pm GHC 7215, or by appointment

Lectures 
Tu,Th 4:30-5:50 pm, GHC 4101


Course Description

CMU 02-518 (12 units) & CMU 02-718 (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: proteomics, genomics, metabolomics, transcriptomics, 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 basic research. Subsequent modules will focus specific clinical tasks, including: phenotyping, biomarker discovery, predictive and causal modeling, and optimizing medical interventions.  Personalized and precision medicine will also be discussed. Class sessions will consist of lectures. Students will be graded based on homeworks and a course project. 

Prerequisites

The course is designed for graduate and upper-level undergraduate students with 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).


Course Requirements: 02-518 (this section is intended for undergraduate students)

  • Homeworks (80%)
    • Four homeworks will be assigned.   
    • Every student is given a budget of three 'late days' to use as they see fit. 
      • There is no need to email the instructor to request a late day  
      • Anything that is more than 15 minutes past the deadline is considered late. 
      • If you hand the assignment more than 24 hrs after the deadline, two late days will be changed, etc.
      • Once you use up your three late days, a 25% per day penalty is applied to the homework grade.  
  • Course Project (20%)  

Grading for 02-518

The following map will be used to determine your final grade:
 Percentage  Letter Grade
 85-100+%
 70-84%
 55-69%C
 40-54% D 
 <40%R


Course Requirements: 02-718 (this section is intended for graduate students)
  • Homeworks (70%) 
    • Five homeworks will be assigned.   
    • Every student is given a budget of three 'late days' to use as they see fit. 
      • There is no need to email the instructor to request a late day  
      • Anything that is more than 15 minutes past the deadline is considered late. 
      • If you hand the assignment more than 24 hrs after the deadline, two late days will be changed, etc.
      • Once you use up your three late days, a 25% per day penalty is applied to the homework grade. 
  • Course Project (30%) 
    • You will complete a data analysis project and write a short report and give a short (10-15 min) presentation.  
    • A project proposal will be due mid-semester. See Proposals for more information.
    • Cheating policy: All work must be your own and novel.  Unauthorized collaboration, falsified data, or plagiarism will result in a failing grade and will be reported to your academic advisor and dean.
    • Double dipping policy:  You may not re-use data, reports, manuscripts, or publications from your research or from other courses. However, you  may extend your previous work, as long as you inform the instructor that you are doing so. Please contact the instructor if you have any questions regarding this policy. 

Grading for 02-718

The following map will be used to determine your final grade:
 Percentage   Letter Grade
97-100%  A+
 93-96% A  
90-92%  A-
 87-89% B+
 83-86% B 
 80-82% B-
 77-79% C+
 73-76% C 
 70-72% C-
 60-69% D 
 <60% R 


Course Outline

  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
    • Causal Modeling
    • Optimizing and/or selecting interventions
  3. Advanced topics (2 weeks)

Required Text

None

Course Outcomes

Students who complete the course successfully will be able to:
  • Explain the core computational challenges in medicine 
  • Explain common clinical data types, and what they are used for
  • Discuss and critique computational methods used to address these challenges
  • Use software to analyze clinical data and build predictive models


Your well-being

Be sure to take care of yourself.  Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:

CaPS: 412-268-2922

Resolve Crisis Network: 888-796-8226

If the situation is life threatening, call the police:

           On campus: CMU Police: 412-268-2323

           Off campus: 911

If you have questions about this or your coursework, please let me know.