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.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Clinical Natural Language Processing

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Clinical Natural Language Processing

This course is part of Clinical Data Science Specialization

Instructor: Laura K. Wiley, PhD

6,457 already enrolled

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

23 reviews

Intermediate level

Some related experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

23 reviews

Intermediate level

Some related experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll 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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments¹

AI Graded see disclaimer

Taught in English

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Build your subject-matter expertise

This course is part of the Clinical Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

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.

Module details

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.

What's included

7 videos4 readings1 assignment

7 videosTotal 20 minutes

Welcome to Clinical Natural Language Processing2 minutes
Introduction to Clinical Natural Language Processing3 minutes
NLP Fundamentals: Linguistics2 minutes
NLP Fundamentals: Morphology & Lexicography4 minutes
NLP Fundamentals: Syntax5 minutes
NLP Fundamentals: Sematics & Pragmatics4 minutes
NLP Fundamentals: Wrap Up1 minute

4 readingsTotal 30 minutes

Get help and meet other learners in this course. Join your discussion forums!5 minutes
Introduction to Specialization Instructors5 minutes
Course Policies5 minutes
Accessing Course Data and Technology Platform15 minutes

1 assignmentTotal 20 minutes

Week 1 Assessment20 minutes

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.

What's included

3 videos2 readings2 assignments

3 videosTotal 17 minutes

Introduction to Regular Expressions8 minutes
Text Processing in the Tidyverse4 minutes
Tips and Tricks for Text Processing5 minutes

2 readingsTotal 62 minutes

Regular Expressions and Text Processing in R60 minutes
Note about the Assessment2 minutes

2 assignmentsTotal 70 minutes

Regular Expressions and Text Processing in R - Try it Out For Yourself Exercises40 minutes
Week 2 Assessment30 minutes

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.

What's included

4 videos2 readings2 assignments

4 videosTotal 14 minutes

Techniques: Note Sections6 minutes
Clinical Note Types: History and Physical Notes3 minutes
Clinical Note Types: Discharge Summaries3 minutes
Clinical Note Types: Radiology Reports2 minutes

2 readingsTotal 92 minutes

Note Section Techniques90 minutes
Note about the Assessment2 minutes

2 assignmentsTotal 75 minutes

Note Section Techniques - Try It Out For Yourself Excercises45 minutes
Week 3 Assessment30 minutes

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.