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The widespread adoption of electronic health records (EHRs) has created a new source of “bigdata”—namely, the record of routine clinical practice—as a by-product of care. Can we use this data to save lives and promote wellbeing?

Upon completing this course, you should be able to:

  1. differentiate between and give examples of categories of research questions and the study designs used to address them.
  2. describe common healthcare data sources and their relative advantages and limitations.
  3. extract and transform various kinds of clinical data to create analysis-ready datasets.
  4. design and execute a statistical analysis of a clinical dataset to answer a research question.
  5. apply your knowledge to evaluate and criticize published clinical informatics research.

The overall goal of this course is to prepare you to discover meaningful clinical knowledge using healthcare data. In addition, 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.

This course is also offered in a two credit version (BIOMEDIN 225) which meets at the same time and requires completing two home work assignments, journal club, and the in-class writing assignment.

Prerequisites: CS 106A; familiarity with statistics (STATS 60 or equivalent) and biology.
Highly recommended: STATS 216.
Recommended: CS 145 or CS 246, CS 229, STATS 305 or HRP 258.

Schedule and Syllabus

Schedule: TUE, THU 1:30 PM - 2:50 PM
Lectures: Gates B03. Lectures are recorded
Videos: Posted on Canvas about two hours after the class ends
Office Hours: Wednesdays 3 - 4:30 PM, in MSOB room 275 (see class announcements)
Syllabus : 2018 Syllabus link (log in with you G-Suite account)
Course materials: Go to and join BIOMEDIN 215

TAs: Stephen Pfohl (spfohl AT, Craig Smail (csmail AT, Ben Huynh (benhuynh AT

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biomedin215.txt · Last modified: 2019/07/26 15:31 by nigam