Data, Models, and Applications to Healthcare Analytics

Description

Healthcare analytics examines patterns in various healthcare data in order to improve administrative costs, clinical decision support, care coordination and patient wellness. This data field is a growing industry in the United States that is expected to grow more than $18.7 billion by 2020.

In this course, students will understand the fundamentals of data science and learn about biological and statistical models. The course will then delve into applications to medical product safety evaluation and health risk models. In addition to learning about theoretical statistical models, applications to environmental health, nutritional epidemiology, wellness and prevention will be discussed.

Prerequisites

Graduate students: STATS202 or STATS216, or CS229.

A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better.

Undergraduate students: consent of instructor.

Topics include

• Overview of medical product evaluation, from pre-licensure clinical trials to post-marketing observational studies
• Drug and adverse event dictionaries
• Multiplicity in the evaluation of clinical safety data
• Pharmacokinetic-pharmacodynamic models
• Classification and regression trees, support vector machines, gradient boosting and neural networks
• Benefit-risk assessment and multi-criteria decision analytics
• Meta-analysis of multiple safety studies

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.