This course is part of the Clinical Data Science Specialization

Laura K. Wiley, PhD

1,675 already enrolled

Offered By

Clinical Data Science SpecializationUniversity of Colorado System

About this Course

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This course teaches you the fundamentals of transforming clinical practice using predictive models. This course examines specific challenges and methods of clinical implementation, that clinical data scientists must be aware of when developing their predictive models.

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Course 5 of 6 in the

Clinical Data Science Specialization

Intermediate Level

Approx. 11 hours to complete

English

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 5 of 6 in the

Clinical Data Science Specialization

Intermediate Level

Approx. 11 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Laura K. Wiley, PhD

Assistant Professor

Division of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus

23,390 Learners

6 Courses

Offered by

University of Colorado System

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Syllabus - What you will learn from this course

Week1

Week 1

1 hour to complete

Introduction: Clinical Prediction Models

1 hour to complete

8 videos (Total 27 min), 3 readings, 1 quiz

8 videos

Welcome to Predictive Modeling and Transforming Clinical Practice1mSetting Expectations2mIntroduction to Clinical Prediction Models8mTypes of Clinical Prediction Models1mClinical Prediction Models5mOperational Prediction Models2mFinancial Prediction Models1mDeveloping Clinical Prediction Models3m

3 readings

Introduction to Specialization Instructors5mCourse Policies5mAccessing Course Data and Technology Platform15m

1 practice exercise

Week 1 Assessment30m

Week2

Week 2

3 hours to complete

Tools: Ensuring Model Usability

3 hours to complete

9 videos (Total 46 min)

9 videos

Ensuring Model Usability4mIntroduction to Qualitative Methods3mQualitative Methods: Choosing the Right Tool3mQualitative Methods: Population Selection8mQualitative Methods: Data Collection10mQualitative Methods: Data Analysis4mQualitative Methods for Transforming Clinical Practice4mIntroduction to Workflow Observations4mQualitative Project Introduction2m

1 practice exercise

Week 2 Assessment30m

Week3

Week 3

1 hour to complete

Techniques: Model Implementation and Sustainability

1 hour to complete

5 videos (Total 28 min)

Week4

Week 4

2 hours to complete

Techniques: Data Selection, Model Building, and Evaluation

2 hours to complete

8 videos (Total 18 min), 2 readings, 2 quizzes

8 videos

Building Prediction Models55sClinical Data for Prediction Models39sClinical Prediction Models: Encounters3mClinical Prediction Models: Billing Data1mClinical Prediction Models: Laboratory Data3mClinical Prediction Models: Medications4mClinical Prediction Models: Clinical Observations1mClinical Prediction Models: Demographics, Health, Social, & Family History3m

2 readings

Building a Clinical Prediction Model1hNote about the Assessment2m

2 practice exercises

Building a Clinical Prediction Model - Try it Out For Yourself Exercise30mWeek 4 Assessment30m

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Predictive Modeling and Transforming Clinical Practice

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