Clinical Natural Language Processing
Master clinical NLP techniques for processing medical text data. Learn regular expressions, text mining, and practical applications for healthcare analytics.
Master clinical NLP techniques for processing medical text data. Learn regular expressions, text mining, and practical applications for healthcare analytics.
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.
3.6
(22 ratings)
5,679 already enrolled
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What you'll learn
Write and implement regular expressions for clinical text analysis
Process and analyze medical note sections effectively
Develop text mining algorithms for healthcare applications
Extract meaningful information from clinical documentation
Implement keyword-based text analysis techniques
Skills you'll gain
This course includes:
1 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 focuses on clinical natural language processing (NLP), teaching fundamental linguistic principles and practical techniques for processing medical text data. Students learn to write regular expressions, handle text data in R, and extract information from clinical notes. The course culminates in a real-world application project where learners develop algorithms to identify diabetic complications from clinical notes. The course utilizes Google Cloud's computational environment for hands-on practice with actual clinical data.
Introduction: Clinical Natural Language Processing
Module 1 · 1 Hours to complete
Tools: Regular Expressions
Module 2 · 2 Hours to complete
Techniques: Note Sections
Module 3 · 3 Hours to complete
Techniques: Keyword Windows
Module 4 · 3 Hours to complete
Practical Application: Identifying Patients with Diabetic Complications
Module 5 · 2 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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3.6 course rating
22 ratings
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