University of Colorado System

Identifying Patient Populations

This course is part of Clinical Data Science Specialization

Taught in English

Some content may not be translated

Laura K. Wiley, PhD

Instructor: Laura K. Wiley, PhD

3,206 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(37 reviews)

Intermediate level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace

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.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(37 reviews)

Intermediate level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Clinical Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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 quizzes

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 quizzes

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 quizzes

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 quiz

Put your new skills to the test - develop an computational phenotyping algorithm to identify patients with hypertension.

What's included

1 reading1 peer review

Instructor

Instructor ratings
4.7 (6 ratings)
Laura K. Wiley, PhD
University of Colorado System
6 Courses26,992 learners

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Recommended if you're interested in Data Analysis

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4.5

37 reviews

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AB
5

Reviewed on May 12, 2019

FG
4

Reviewed on Dec 12, 2019

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4

Reviewed on Apr 4, 2019

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