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.
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.
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.
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.
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.
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.
Practical Application: Identifying Patients with Diabetic Complications
Apply the tools and techniques that you have learned in the course to a real-world example!