This course, offered by University of Colorado System, delves into the fundamentals of clinical natural language processing (NLP). Through a combination of theoretical learning and hands-on practice, you will gain a deep understanding of linguistic principles and the application of regular expressions in clinical text analysis.
Key learning points include recognizing and distinguishing the complexities of text mining, selecting note sections for analysis, and utilizing R for text processing. The course culminates in a practical project where you will develop text processing algorithms to identify diabetic complications from clinical notes, using a free computational environment provided by Google Cloud.
Certificate Available ✔
Get Started / More InfoThis course is divided into five modules, covering NLP fundamentals, regular expressions, note sections, keyword windows, and a practical application to identify patients with diabetic complications from clinical notes.
Welcome to Clinical Natural Language Processing! This module provides an introduction to the course and the instructors, as well as an overview of course policies and accessing course data and technology platform. You will also undertake the Week 1 assessment.
In this module, you will gain an understanding of regular expressions and their application in text processing using R. You will explore tips and tricks for text processing and complete the Week 2 assessment, followed by exercises to apply regular expressions in R.
Here, you will learn about different clinical note types and techniques for selecting note sections that can be used to answer analytic questions. The module includes the Week 3 assessment and practical exercises to reinforce your learning.
This module focuses on techniques for creating keyword windows to search for other keywords and phrases to answer analytic questions. It concludes with the Week 4 assessment and practical exercises with answers to further enhance your skills.
Engage in a practical application project where you will spend 150 minutes identifying patients with diabetic complications from clinical notes, integrating all the knowledge and skills acquired throughout the course.
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