Course Meeting Times
Lectures: 1 session / week, 1.5 hours / session
Who Should Take this Course?
The course is required for students in the Master's Program in Medical Informatics, but is open to other graduate students and advanced undergraduates.
Below are some examples of projects developed in this course, by domain:
- Anonymization of Databases
- Using Boolean Reasoning to Anonymize Databases
- Diagnostic Models
- Using Patient-Reportable Clinical History Factors to Predict Myocardial Infarction
- A Genetic Algorithm to Select Variables in Logistic Regression: Example in the Domain of Myocardial Infarction
- Prognostic Models
- Development and Evaluation of Models to Predict Death and Myocardial Infarction Following Coronary Angioplasty and Stenting
- Major Complications after Angioplasty in Patients with Chronic Renal Failure: A Comparison of Predictive Models
There is no required textbook for this course, however, the following textbooks are recommended for reference:
Hastie, T., R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. (Springer Series in Statistics.) New York, NY: Springer Verlag, July 30, 2003. ISBN: 9780387952840 .
Familiarity with SAS and MATLAB® will be helpful, and consultation with respective user manuals may be necessary.
Homework's are due at the end of each module. They may require programming, and all code must be included. They are to be solved individually. Homework's received after the deadline may be subject to substantial grade penalty. No homework's will be accepted after the solutions have been handled.
The midterm will contain topics from decision analysis and machine learning. There will be a strict time limit of 1.5 h. Students should bring class notes, homework solutions, and readings.
40% Final Project
The final project has to be developed for this course and should be done individually. Although the project may contain parts developed previously for another purpose, it is essential that substantial effort be demonstrated into developing a project specifically for this class. A final report in the form of a 5+ page paper is expected, as well as a demonstration of the implementation in the form of a 15 minute presentation. Students are advised to discuss their projects with the instructors ahead of time. Examples of final reports will be handled during the course.