This course is part of the Clinical Data Science Specialization

Laura K. Wiley, PhD

Offered By

Clinical Data Science SpecializationUniversity of Colorado System

About this Course

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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.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Course 4 of 6 in the

Clinical Data Science Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 12 hours to complete

English

Subtitles: English

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.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 6 in the

Clinical Data Science Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 12 hours to complete

English

Subtitles: English

Instructor

Laura K. Wiley, PhD

Assistant Professor

Division of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus

14,126 Learners

6 Courses

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

Week

Week 1

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

Welcome to Clinical Natural Language Processing1m

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

Week

Week 2

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

Introduction to Regular Expressions7m

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

Week

Week 3

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

Techniques: Note Sections5m

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

Week

Week 4

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

Techniques: Keyword Windows5m

2 readings

Keyword Windows Techniques2h

Note about the Assessment2m

2 practice exercises

Keyword Windows Techniques - Try it Out For Yourself Answers45m

Week 4 Assessment30m

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?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

Clinical Natural Language Processing