This course teaches you the fundamentals of clinical natural language processing (NLP). In this course you will learn the basic linguistic principals underlying NLP, as well as how to write regular expressions and handle text data in R. You will also learn practical techniques for text processing to be able to extract information from clinical notes. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop text processing algorithms to identify diabetic complications from clinical notes. You will complete this work using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.
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Clinical Natural Language Processing
University of Colorado SystemAbout this Course
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Intermediate Level
Approx. 12 hours to complete
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 12 hours to complete
English
Subtitles: English
Offered by
University of Colorado System
The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond.
Syllabus - What you will learn from this course
1 hour to complete
Introduction: Clinical Natural Language Processing
This module covers the basics of text mining, text processing, and natural language processing. It also provides a information on the linguistic foundations that underly NLP tools.
1 hour to complete
7 videos (Total 20 min), 3 readings, 1 quiz
7 videos
Introduction to Clinical Natural Language Processing3m
NLP Fundamentals: Linguistics1m
NLP Fundamentals: Morphology & Lexicography4m
NLP Fundamentals: Syntax4m
NLP Fundamentals: Sematics & Pragmatics4m
NLP Fundamentals: Wrap Up40s
3 readings
Introduction to Specialization Instructors5m
Course Policies5m
Accessing Course Data and Technology Platform15m
1 practice exercise
Week 1 Assessment20m
2 hours to complete
Tools: Regular Expressions
This module introduces regular expressions, the method of text processing, and how to work with text data in R. Mastery is demonstrated through a programming assignment with applied questions.
2 hours to complete
3 videos (Total 17 min), 2 readings, 2 quizzes
3 videos
Text Processing in the Tidyverse3m
Tips and Tricks for Text Processing5m
2 readings
Regular Expressions and Text Processing in R1h
Note about the Assessment2m
2 practice exercises
Regular Expressions and Text Processing in R - Try it Out For Yourself Exercises40m
Week 2 Assessment30m
3 hours to complete
Techniques: Note Sections
This module discusses how the section of a clinical note can affect the meaning of text in the section. A programming assignment provides hands on practice with how to apply this knowledge to process clinical text.
3 hours to complete
4 videos (Total 14 min), 2 readings, 2 quizzes
4 videos
Clinical Note Types: History and Physical Notes2m
Clinical Note Types: Discharge Summaries2m
Clinical Note Types: Radiology Reports2m
2 readings
Note Section Techniques1h 30m
Note about the Assessment2m
2 practice exercises
Note Section Techniques - Try It Out For Yourself Excercises45m
Week 3 Assessment30m
3 hours to complete
Techniques: Keyword Windows
This module discusses how you can build windows of text around keywords of interest to understand the context and meaning of how the keyword is being used. A programming assignment provides hands on practice with how to apply this technique to process clinical text.
3 hours to complete
1 video (Total 5 min), 2 readings, 2 quizzes
1 video
2 readings
Keyword Windows Techniques2h
Note about the Assessment2m
2 practice exercises
Keyword Windows Techniques - Try it Out For Yourself Answers45m
Week 4 Assessment30m
Reviews
TOP REVIEWS FROM CLINICAL NATURAL LANGUAGE PROCESSING
by VCMay 21, 2020
Excellent course. Well paced, well thoughtout and put together.
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I live in an area that restricts access to Google products. Will I be able to complete the specialization?
Unfortunately at this time we can only allow students who have access to Google services (e.g., a gmail account) to complete the specialization. This is because we give students access to real clinical data and our privacy protections only allow data sharing through the Google BigQuery environment.
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