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
Leader in Biomedical Informatics and Precision Medicine at the University of Colorado Anschutz Medical Campus
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 enhance precision medicine through the development of computational phenotyping algorithms and innovative approaches to clinical data science.Dr. Wiley has led significant projects, including work on precision dosing algorithms for warfarin in African Americans and serving as the lead informatician on a comprehensive tobacco cessation service funded by the NIH Cancer Moonshot initiative. She is a principal investigator in the Colorado Center for Personalized Medicine and has published extensively on topics related to health informatics and medical technology.In addition to her research, Dr. Wiley is actively involved in the American Medical Informatics Association (AMIA), having chaired various summits and served on the board of directors. She has co-developed the Coursera Clinical Data Science Specialization, which includes courses designed to teach essential skills in clinical research informatics.Her courses on Coursera include "Introduction to Clinical Data Science," "Advanced Clinical Data Science," and "Predictive Modeling and Transforming Clinical Practice," aimed at equipping students with the knowledge necessary for data-driven healthcare solutions
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