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.
Clinical Natural Language Processing
9 個評分
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Clinical Natural Language Processing
科罗拉多大学系统
課程信息
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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.
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中級
完成時間大約為12 小時
英語（English）
字幕：英語（English）
教學大綱 - 您將從這門課程中學到什麼
完成時間為 1 小時
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.
完成時間為 1 小時
7 個視頻 （總計 20 分鐘）, 3 個閱讀材料, 1 個測驗
7 個視頻
Introduction to Clinical Natural Language Processing3分鐘
NLP Fundamentals: Linguistics1分鐘
NLP Fundamentals: Morphology & Lexicography4分鐘
NLP Fundamentals: Syntax4分鐘
NLP Fundamentals: Sematics & Pragmatics4分鐘
NLP Fundamentals: Wrap Up40
3 個閱讀材料
Introduction to Specialization Instructors5分鐘
Course Policies5分鐘
Accessing Course Data and Technology Platform15分鐘
1 個練習
Week 1 Assessment20分鐘
完成時間為 2 小時
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.
完成時間為 2 小時
3 個視頻 （總計 17 分鐘）, 2 個閱讀材料, 2 個測驗
3 個視頻
Text Processing in the Tidyverse3分鐘
Tips and Tricks for Text Processing5分鐘
2 個閱讀材料
Regular Expressions and Text Processing in R1小時
Note about the Assessment2分鐘
2 個練習
Regular Expressions and Text Processing in R - Try it Out For Yourself Exercises40分鐘
Week 2 Assessment30分鐘
完成時間為 3 小時
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.
完成時間為 3 小時
4 個視頻 （總計 14 分鐘）, 2 個閱讀材料, 2 個測驗
4 個視頻
Clinical Note Types: History and Physical Notes2分鐘
Clinical Note Types: Discharge Summaries2分鐘
Clinical Note Types: Radiology Reports2分鐘
2 個閱讀材料
Note Section Techniques1 小時 30 分
Note about the Assessment2分鐘
2 個練習
Note Section Techniques - Try It Out For Yourself Excercises45分鐘
Week 3 Assessment30分鐘
完成時間為 3 小時
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.
完成時間為 3 小時
1 個視頻 （總計 5 分鐘）, 2 個閱讀材料, 2 個測驗
1 個視頻
2 個閱讀材料
Keyword Windows Techniques2小時
Note about the Assessment2分鐘
2 個練習
Keyword Windows Techniques - Try it Out For Yourself Answers45分鐘
Week 4 Assessment30分鐘
關於 科罗拉多大学系统
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.
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退款政策是如何规定的？
有助学金吗？
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.
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