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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/clinical-natural-language-processing
课程评论：没有评论
课程名称：临床自然语言处理 概述：本课程教授临床自然语言处理（NLP）的基本原理。您将学习NLP的基本语言学原则，以及如何在R中编写正则表达式和处理文本数据。课程还将介绍实用的文本处理技术，以便从临床记录中提取信息。最后，您将有机会通过一个实际应用来检验您的技能，开发文本处理算法以从临床记录中识别糖尿病并发症。该工作将在我们的行业合作伙伴Google Cloud提供的免费在线数据科学计算环境中完成。 课程大纲： 1. **介绍：临床自然语言处理** - 描述：本模块涵盖文本挖掘、文本处理和自然语言处理的基础知识，并提供NLP工具的语言学基础信息。 2. **工具：正则表达式** - 描述：本模块介绍正则表达式这一文本处理方法，以及如何在R中处理文本数据。通过编程作业来展示对该技能的掌握。 3. **技术：记录部分** - 描述：本模块讨论临床记录的不同部分如何影响文本意义。编程作业提供实践机会，以将这些知识应用于临床文本处理。 4. **技术：关键词窗口** - 描述：本模块讨论如何围绕感兴趣的关键词构建文本窗口，以理解关键词的使用上下文和含义。编程作业提供实践机会，以应用该技术处理临床文本。 5. **实际应用：识别糖尿病并发症患者** - 描述：将您在课程中学到的工具和技术应用于一个真实案例！ 这个课程将帮助您掌握临床NLP的核心概念并获得实际操作的经验。
Name:Introduction: Clinical Natural Language Processing
Description: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.
Name:Tools: Regular Expressions
Description: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.
Name:Techniques: Note Sections
Description: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.
Name:Techniques: Keyword Windows
Description: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.
Name:Practical Application: Identifying Patients with Diabetic Complications
Description:Apply the tools and techniques that you have learned in the course to a real-world example!
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.
