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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. To have the best learning experience, you will need to be proficient in the R language.

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 (MSOB room 393): Tuesday 11am-12pm (Minh); 4-5pm (Ben) | Thursday 9-10am (Conor)
Syllabus : 2019 Syllabus link
Course materials: Go to and join BIOMEDIN 215

TAs: Ben Huynh (benhuynh AT, Conor Corbin (ccorbin AT, Minh Nguyen (minh084 AT

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biomedin215_precovid.txt · Last modified: 2020/06/24 16:24 by nigam