This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.
Data quality dimensions / fitness for use
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Skills You'll Learn
Databases, Data Management, Data Structures
Reviews
4.2 (63 ratings)
- 5 stars52.38%
- 4 stars26.98%
- 3 stars11.11%
- 2 stars4.76%
- 1 star4.76%
MK
Nov 5, 2019
What a great course!! Kudos to the professor for being so detail oriented!! I learned a great deal about the clinical data models from this course!!
JJ
Sep 14, 2019
Good instructor who took time to explain and walked through each steps of the ETL process. Highly recommended.
From the lesson
Techniques: Data Quality Assessments
We explore the dimensions of data quality by reviewing its challenges, data quality measurements used to measure it, and data quality rules to assess its acceptability for use.
Taught By
Laura K. Wiley, PhD
Associate Professor
Michael G. Kahn, MD, PhD
Professor of Clinical Informatics