- Admin: Yasii Mirdamadi
- Kostas Christidis, IBM & NC State
- TBD
- Hock 2nd Floor, CRTP Classroom
- Fridays 11:00 to 14:30 (Lunch 12:00 to 13:00)
- Saturdays 08:00 to 10:45
To understand where the tools of data science intersect with healthcare. Exposure to principles of machine learning with practical contact using the Microsoft Azure ML platform. Exposure to technologies such as Infastructure-as-a-Service, architectural principles such as service-oriented architecture and microservices. Use cases where data science paradigms and technologies have or have the potential to deliver real world impact.
This course is designed to introduce students to the tools and technologies of “data science” as they are applied in healthcare. Bill Cleveland, the famous computer scientist wrote that "Knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited. A merger of the knowledge bases would produce a powerful force for innovation.” Everything we do in delivering health to our patients involves information: how it’s stored, how it’s moved around, how we extract meaning from it. Understanding the many principles, technologic, ethical, and regulatory issues surrounding this “merger”, is a requirement for leadership in the realm of biomedical informatics. Included in this course will be practical hands-on experience with plug and play machine learning tools via Microsoft Azure (no programming needed). Text for the class is Machine Learning: The new AI, Ethem Alpaydin.
The primary goal for the course is to help students think critically about how technology helps us solve problems for our patients. In an era of "Precision Medicine" that focuses on "getting the right therapy to the right patient at the right time", our aim is to provide a knowledge base with which to think critically about how technology and data science help us achieve that aim at the point of care or in the operational aspects of health delivery.
- Book can be purchased from Amazon (see link below)
- All readings should be accessible via the Duke network, therefore VPN in (so you have a Duke IP address) to access and download the readings from the links. Obviously, if you're on campus, you shouldn't have to VPN in.
- PDFs of readings are also available in this Box folder: https://duke.box.com/s/3jxzvd0d13xkeisp8dgtw2jht4yqczre
Class attendance is mandatory. Attendance will be taken at every class. If you miss a class, you need to make up the class session. This can be accomplished by reviewing the recorded class lecture and submitting a page synopsis of the class to the professor within one week. The one page synopsis needs to contain references. If you miss a class without prior approval by faculty, there will be a reduction in your debate/discussion overall grade.
- One missed class — 5% reduction in grade
- Two missed classes — 10% reduction in grade
- Three missed classes — 20% reduction in grade
- Four or more missed classes will result in zero grade Class discussion is an essential element of this course. We will have written cases to discuss as well as time to review some of the major learning from team homework assignments.
| ITEM | DUE DATE | PERCENTAGE |
|---|---|---|
| Group Version Control write up (individual grade) | 7 July 2018 | 20% |
| ML Written Assignment (individual grade) | 3 August 2018 | 40% |
| Oxford-Style Debate tourney (group grade) | 3-4 August 2018 | 20% |
| Class Participation (individual grade) | Term | 20% |
It is an expectation that everyone will be on time to class. If you are tardy for 3 or more classes without prior approval from faculty, this will also reflect in you discussion grade.
All course assignments (including presentations that will be presented in class) should be emailed directly to me ([email protected])
Plase review the Code of Professional Conduct for the Duke University School of Medicine here
"The Duke University School of Medicine strives to educate health professional students who have a high capacity for ethical professional behavior. Since training in professional behavior is a part of training in the health professions enrolled students commit themselves to comply with all regulations regarding conduct established by Duke University (the Community Standard and the Bulletin of Information and Regulations of Duke University), the School of Medicine and the individualʹs own academic program, as well as the Social Media Policy of the Duke University Health System (link). Professionalism is an academic issue and failure to demonstrate prescribed professional standards may jeopardize advancement and graduation in the same way as other academic matters These standards closely follow those established and expected for the medical profession for which the student is training and are intended to serve as a precursor to future professional expectations."