This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension. You will complete this work using a real clinical data set while using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.
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Identifying Patient Populations
This course is part of Clinical Data Science Specialization
Instructor: Laura K. Wiley, PhD
3,446 already enrolled
Included with 
(39 reviews)
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What you'll learn
Create a computational phenotyping algorithm
Assess algorithm performance in the context of analytic goal.
Create combinations of at least three data types using boolean logic
Explain the impact of individual data type performance on computational phenotyping.
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There are 5 modules in this course
Learn about computational phenotyping and how to use the technique to identify patient populations.
What's included
5 videos10 readings2 assignments
Understand how different clinical data types can be used to identify patient populations. Begin developing a computational phenotyping algorithm to identify patients with type II diabetes.
What's included
5 videos2 readings2 assignments
Learn how to manipulate individual data types and combine multiple data types in computational phenotyping algorithms. Develop a more sophisticated computational phenotyping algorithm to identify patients with type II diabetes.
What's included
2 videos2 readings2 assignments
Understand how to select a single "best" computational phenotyping algorithm. Finalize and justify a phenotyping algorithm for type II diabetes.
What's included
1 video1 reading1 assignment
Put your new skills to the test - develop an computational phenotyping algorithm to identify patients with hypertension.
What's included
1 reading1 peer review
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Offered by
Recommended if you're interested in Data Analysis
Coursera Project Network
University of Illinois Urbana-Champaign
The State University of New York
Northeastern University
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Frequently asked questions
Unfortunately at this time we can only allow students who have access to Google services (e.g., a gmail account) to complete the specialization. This is because we give students access to real clinical data and our privacy protections only allow data sharing through the Google BigQuery environment.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.