Predictive Modeling and Transforming Clinical Practice
Learn to develop and implement predictive models that transform healthcare through data-driven clinical decision support.
Learn to develop and implement predictive models that transform healthcare through data-driven clinical decision support.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Clinical Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Design effective clinical prediction models
Implement qualitative methods for model development
Understand clinical decision support systems
Evaluate model sustainability and effectiveness
Build practical prediction models using clinical data
Transform clinical practice through data science
Skills you'll gain
This course includes:
1.95 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course focuses on developing and implementing predictive models in clinical settings. Students learn about different types of clinical prediction models, qualitative methods for ensuring model usability, implementation techniques, and practical approaches to model building. The curriculum includes hands-on experience with real clinical data, culminating in a project developing an ICU mortality risk prediction model.
Introduction: Clinical Prediction Models
Module 1 · 1 Hours to complete
Tools: Ensuring Model Usability
Module 2 · 2 Hours to complete
Techniques: Model Implementation and Sustainability
Module 3 · 57 Minutes to complete
Techniques: Data Selection, Model Building, and Evaluation
Module 4 · 2 Hours to complete
Practical Application: Developing a Clinical Prediction Model
Module 5 · 4 Hours to complete
Fee Structure
Instructor
Associate Professor
Dr. Laura K. Wiley is an Associate Professor in the Department of Biomedical Informatics at the University of Colorado Anschutz Medical Campus, where she also serves as Chief Data Scientist for Health Data Compass. Her research focuses on leveraging electronic health record (EHR) data to advance precision medicine, including the development of computational phenotyping algorithms for EHR-linked biobanks and exploring precision dosing algorithms for warfarin in diverse populations. Dr. Wiley has played a pivotal role as the lead informatician on an NIH Cancer Moonshot-funded project aimed at creating a comprehensive tobacco cessation service at the University of Colorado Cancer Center.In addition to her research contributions, Dr. Wiley is actively involved in education, co-developing the Clinical Data Science Specialization on Coursera, which comprises a series of courses designed to equip learners with essential skills in clinical research informatics. Her courses include Advanced Clinical Data Science, Introduction to Clinical Data Science, and Predictive Modeling and Transforming Clinical Practice. With a strong commitment to improving patient care through data-driven solutions, Dr. Wiley is recognized as a leader in her field, having served on various boards and committees within the American Medical Informatics Association.
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