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.
4.5
(39 ratings)
3,425 already enrolled
Instructors:
English
21 languages available
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
Innovator in Precision Medicine and Biomedical Informatics
Dr. Laura K. Wiley is an Associate Professor in the Department of Biomedical Informatics at the University of Colorado Anschutz Medical Campus, where she focuses on leveraging electronic health records (EHR) for precision medicine discovery and implementation. With a Ph.D. in Human Genetics from Vanderbilt University, Dr. Wiley has developed computational phenotyping algorithms for EHR-linked biobanks and explored precision dosing of medications like warfarin, particularly in African American populations. She serves as the Chief Data Scientist for Health Data Compass, a cloud-based research data warehouse that integrates data from multiple institutions to enhance clinical research capabilities. Dr. Wiley has been actively involved in various initiatives, including a NIH Cancer Moonshot-funded project aimed at creating comprehensive tobacco cessation services at the University of Colorado Cancer Center. Her contributions to the field are recognized through her leadership roles in the American Medical Informatics Association and her involvement in developing the Coursera Clinical Data Science Specialization, which educates students on clinical research informatics skills. Through her research and teaching, Dr. Wiley is dedicated to advancing the integration of data science in healthcare to improve patient outcomes and foster innovation in medical practices.
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4.5 course rating
39 ratings
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