Identifying Patient Populations
Master computational phenotyping techniques to identify and analyze patient populations using clinical data science.
Master computational phenotyping techniques to identify and analyze patient populations using clinical data science.
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.
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What you'll learn
Create and validate computational phenotyping algorithms
Analyze multiple clinical data types effectively
Develop complex boolean logic combinations
Assess algorithm performance and accuracy
Implement manual record review techniques
Skills you'll gain
This course includes:
1.2 Hours PreRecorded video
7 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course teaches the fundamentals of computational phenotyping for identifying patient populations. Students learn to evaluate different clinical data types, develop algorithms, and assess their performance. Using Google Cloud's platform and real clinical datasets, learners gain practical experience in creating and validating phenotyping algorithms, with a special focus on identifying conditions like hypertension and diabetes. The course combines theoretical knowledge with hands-on programming exercises.
Introduction: Identifying Patient Populations
Module 1 · 2 Hours to complete
Tools: Clinical Data Types
Module 2 · 2 Hours to complete
Techniques: Data Manipulations and Combinations
Module 3 · 3 Hours to complete
Techniques: Algorithm Selection and Portability
Module 4 · 0 Hours to complete
Practical Application: Develop a Computational Phenotyping Algorithm to Identify Patients with Hypertension
Module 5 · 3 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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