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.
This course is part of the Clinical Data Science Specialization
Clinical Natural Language Processing
Offered By
About this Course
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What you will learn
Recognize and distinguish the difference in complexity and sophistication of text mining, text processing, and natural language processing.
Write basic regular expressions to identify common clinical text.
Assess and select note sections that can be used to answer analytic questions.
Write R code to search text windows for other keywords and phrases to answer analytic questions.
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Intermediate Level
Approx. 21 hours to complete
Suggested: 5 weeks of study, 2-4 hours/week
English
Subtitles: English
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
Instructor
Laura K. Wiley, PhD
Assistant ProfessorDivision of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus
About 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.
About the Clinical Data Science Specialization
Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist!
This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data.
By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data.
This specialization is supported by our industry partnership with Google Cloud. Thanks to this support, all learners will have access to a fully hosted online data science computational environment for free! Please note that you must have access to a Google account (i.e., gmail account) to access the clinical data and computational environment.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
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.
More questions? Visit the Learner Help Center.
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