About this Course
4,522

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Some programming experience in any language.

Approx. 24 hours to complete

Suggested: 5 weeks of study, 2-4 hours/week...

English

Subtitles: English

What you will learn

  • Check

    Create a computational phenotyping algorithm

  • Check

    Assess algorithm performance in the context of analytic goal.

  • Check

    Create combinations of at least three data types using boolean logic

  • Check

    Explain the impact of individual data type performance on computational phenotyping.

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Some programming experience in any language.

Approx. 24 hours to complete

Suggested: 5 weeks of study, 2-4 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Introduction: Identifying Patient Populations

Learn about computational phenotyping and how to use the technique to identify patient populations. ...
5 videos (Total 23 min), 9 readings, 2 quizzes
5 videos
Introduction to Computational Phenotyping5m
Introduction to Manual Record Review4m
Manual Record Review: Selecting Reviewers and Records6m
Manual Record Review: Tools and Techniques5m
9 readings
Introduction to Specialization Instructors5m
Course Policies5m
Accessing Course Data and Technology Platform15m
Introduction to Course Example15m
Introduction to Manual Record Review10m
Methods - Selecting Reviewers10m
Methods - Selecting Records for Review10m
Methods - Creating Review Instruments and Protocols10m
Methods - Assessing Review Quality10m
2 practice exercises
Week 1 Practice Quiz8m
Week 1 Assessment16m
Week
2
3 hours to complete

Tools: Clinical Data Types

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....
5 videos (Total 19 min), 2 readings, 2 quizzes
5 videos
Computational Phenotyping: Billing Data5m
Computational Phenotyping: Laboratory Data3m
Computational Phenotyping: Clinical Observations2m
Computational Phenotyping: Medications3m
2 readings
Testing Individual Data Types30m
Note about the Assessment2m
2 practice exercises
Programming Exercises Practice Quiz30m
Week 2 Assessment18m
Week
3
3 hours to complete

Techniques: Data Manipulations and Combinations

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....
2 videos (Total 15 min), 2 readings, 2 quizzes
2 videos
Combining Multiple Data Types5m
2 readings
Data Manipulations30m
Data Combinations45m
2 practice exercises
Programming Exercises Practice Quiz30m
Week 3 Assessment25m
Week
4
1 hour to complete

Techniques: Algorithm Selection and Portability

Understand how to select a single "best" computational phenotyping algorithm. Finalize and justify a phenotyping algorithm for type II diabetes....
1 video (Total 4 min), 1 reading, 1 quiz
1 reading
Assessing Algorithmic Accuracy, Complexity, and Portability25m
1 practice exercise
Week 4 Assessment20m
Week
5
4 hours to complete

Practical Application: Develop a Computational Phenotyping Algorithm to Identify Patients with Hypertension

Put your new skills to the test - develop an computational phenotyping algorithm to identify patients with hypertension. ...
1 reading, 1 quiz
1 reading
Welcome to Practical Applications!5m
4.9
2 ReviewsChevron Right

Top Reviews

By ABMay 13th 2019

This is a well-presented course. I highly recommend.

Instructor

Avatar

Laura K. Wiley, PhD

Assistant Professor
Division of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus

About University of Colorado System

The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond....

About the Clinical Data Science Specialization

Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist! This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data. By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data. This specialization is supported by our industry partnership with Google Cloud. Thanks to this support, all learners will have access to a fully hosted online data science computational environment for free! Please note that you must have access to a Google account (i.e., gmail account) to access the clinical data and computational environment....
Clinical Data Science

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

  • 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.

More questions? Visit the Learner Help Center.