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Clinical Natural Language Processing

Approx. 12 hours to complete

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Course Summary

This course on Clinical Natural Language Processing covers the basics of NLP and its applications in the healthcare industry. Students will learn how to extract relevant information from medical texts and use it to improve patient care.

Key Learning Points

Introduction to Natural Language Processing
Applications of NLP in healthcare
Techniques for extracting information from medical texts

Learning Outcomes

Ability to extract relevant information from medical texts
Understanding of NLP techniques and applications in healthcare
Improved patient care through NLP technology

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming
Familiarity with healthcare terminology

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Hands-on exercises

Similar Courses

Natural Language Processing with Python
Applied Data Science with Python
Data Science in Healthcare

Related Education Paths

Notable People in This Field

Chief Information Officer at Vanderbilt University Medical Center
Chair of the Department of Biomedical Informatics at UC San Diego

Related Books

Description

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.

Knowledge

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.

Outline

Introduction: Clinical Natural Language Processing
Welcome to Clinical Natural Language Processing
Introduction to Clinical Natural Language Processing
NLP Fundamentals: Linguistics
NLP Fundamentals: Morphology & Lexicography
NLP Fundamentals: Syntax
NLP Fundamentals: Sematics & Pragmatics
NLP Fundamentals: Wrap Up
Introduction to Specialization Instructors
Course Policies
Accessing Course Data and Technology Platform
Week 1 Assessment

Tools: Regular Expressions
Introduction to Regular Expressions
Text Processing in the Tidyverse
Tips and Tricks for Text Processing
Regular Expressions and Text Processing in R
Note about the Assessment
Regular Expressions and Text Processing in R - Try it Out For Yourself Exercises
Week 2 Assessment

Techniques: Note Sections
Techniques: Note Sections
Clinical Note Types: History and Physical Notes
Clinical Note Types: Discharge Summaries
Clinical Note Types: Radiology Reports
Note Section Techniques
Note about the Assessment
Note Section Techniques - Try It Out For Yourself Excercises
Week 3 Assessment

Techniques: Keyword Windows
Techniques: Keyword Windows
Keyword Windows Techniques
Note about the Assessment
Keyword Windows Techniques - Try it Out For Yourself Answers
Week 4 Assessment

Practical Application: Identifying Patients with Diabetic Complications
Welcome to Practical Applications!

Summary of User Reviews

This course in clinical natural language processing has received great reviews from users. Students have found it to be a valuable resource for learning about the topic and improving their skills. One key aspect that many users thought was good is the course's focus on practical applications of natural language processing in the healthcare industry.

Pros from User Reviews

The course provides a thorough introduction to the topic of clinical natural language processing
Users appreciate the practical focus of the course and the real-world examples provided
The instructors are knowledgeable and engaging, and provide clear explanations of complex concepts
The course is well-structured and easy to follow
The assignments and quizzes provide opportunities for hands-on learning and reinforce key concepts

Cons from User Reviews

Some users have found the course to be too basic and not challenging enough
The pace of the course may be too slow for some users
Some users have found the course to be too focused on technical details and not enough on practical applications
The course does not cover all aspects of natural language processing, and some users would like to see more in-depth coverage of certain topics

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