This course is designed to prepare you to pose and answer meaningful clinical questions using routinely collected healthcare data. The practical skills you will learn in this class will be applicable to any task involving data manipulation and analysis. The course will use real, de-identified, large size patient datasets for home work projects associated with the course. To have the best learning experience, you will need to be proficient in the R language.

Topics Include

  • Methods for data-mining at the internet scale
  • Handling of large-scale electronic medical records data for machine learning
  • Methods in natural language processing and text-mining applied to medical records
  • Methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies

Upon Completing This Course, You Should Be Able To:

  • Recognize categories of research questions and the study designs used to address them.
  • Describe common healthcare data sources and their relative advantages and limitations.
  • Extract and transform various kinds of clinical data to create analysis-ready datasets.
  • Design and execute an analysis of a clinical dataset to answer a research question.
  • Apply your knowledge to evaluate and criticize published clinical informatics research.