Clinical Natural Language Processing

Start Date: 08/09/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/clinical-natural-language-processing

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About Course

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.

Course Syllabus

Introduction: Clinical Natural Language Processing
Tools: Regular Expressions
Techniques: Note Sections
Techniques: Keyword Windows

Deep Learning Specialization on Coursera

Course Introduction

Clinical Natural Language Processing There is a growing body of evidence that indicates that humans can understand and utilize a wide variety of natural language (NLP) and that this ability is universal. In this course you will learn the essential components of NLP, how to identify trends and issues concerning NLP amongst different languages, what is the role of NLP in different domains of health and what can NLP do for you? This course is designed to address the first question. Our primary objective is to understand the second. This course addresses the topic of semantic and syntactic NLP, the components of NLP in different languages and the role of NLP in different domains of health. We take the learner directly to the second question. We assume that you already have a general understanding of English grammar, pronunciation and spoken communication. If you do not, proceed through the course by building a vocabulary and language profile of common English verbs and nouns. If you do not, proceed through the course by using the resources provided by the course. Upon successful completion of this course, you will be able to: 1. Describe the major elements of English grammar, usage and speech communication 2. Identify common English verbs and nouns 3. Use the English verb present and past participles 4. Use the English verb present and past participles to indicate different types of present and past participles 5. Use the common past and future participles

Course Tag

Related Wiki Topic

Article Example
Outline of natural language processing The following natural language processing toolkits are popular collections of natural language processing software. They are suites of libraries, frameworks, and applications for symbolic, statistical natural language and speech processing.
History of natural language processing The history of natural language processing describes the advances of natural language processing (Outline of natural language processing). There is some overlap with the history of machine translation and the history of artificial intelligence.
Natural language processing Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Challenges in natural language processing frequently involve natural language understanding, natural language generation (frequently from formal, machine-readable logical forms), connecting language and machine perception, managing human-computer dialog systems, or some combination thereof.
Outline of natural language processing The following technologies make natural language processing possible:
Natural language processing in the late 1980s and mid 1990s, much Natural Language Processing research has relied heavily on machine learning.
Outline of natural language processing Natural language processing can be described as all of the following:
Studies in Natural Language Processing Studies in Natural Language Processing is the book series of the
Outline of natural language processing The following outline is provided as an overview of and topical guide to natural language processing:
Outline of natural language processing Natural language processing contributes to, and makes use of (the theories, tools, and methodologies from), the following fields:
Empirical Methods in Natural Language Processing Empirical Methods in Natural Language Processing or EMNLP is a leading conference in the area of Natural Language Processing. EMNLP is organized by the ACM special interest group on linguistic data (SIGDAT).
Outline of natural language processing Natural language processing – computer activity in which computers are entailed to analyze, understand, alter, or generate natural language. This includes the automation of any or all linguistic forms, activities, or methods of communication, such as conversation, correspondence, reading, written composition, dictation, publishing, translation, lip reading, and so on. Natural language processing is also the name of the branch of computer science, artificial intelligence, and linguistics concerned with enabling computers to engage in communication using natural language(s) in all forms, including but not limited to speech, print, writing, and signing.
Natural language processing Formerly, many language-processing tasks typically involved the direct hand coding of rules, which is not in general robust to natural language variation. The machine-learning paradigm calls instead for using statistical inference to automatically learn such rules through the analysis of large "corpora" of typical real-world examples (a "corpus" (plural, "corpora") is a set of documents, possibly with human or computer annotations).
Natural language understanding Natural language understanding (NLU) is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU is considered an AI-hard problem.
Natural language user interface Natural language interfaces are an active area of study in the field of natural language processing and computational linguistics. An intuitive general natural language interface is one of the active goals of the Semantic Web.
Natural language All language varieties of world languages are natural languages, although some varieties are subject to greater degrees of published prescriptivism and/or language regulation than others. Thus nonstandard dialects can be viewed as a wild type in comparison with standard languages. But even an official language with a regulating academy, such as Standard French with the French Academy, is classified as a natural language (for example, in the field of natural language processing), as its prescriptive points do not make it either constructed enough to be classified as a constructed language or controlled enough to be classified as a controlled natural language.
Natural language Controlled natural languages are subsets of natural languages whose grammars and dictionaries have been restricted in order to reduce or eliminate both ambiguity and complexity (for instance, by cutting down on rarely used superlative or adverbial forms or irregular verbs). The purpose behind the development and implementation of a controlled natural language typically is to aid non-native speakers of a natural language in understanding it, or to ease computer processing of a natural language. An example of a widely used controlled natural language is Simplified English, which was originally developed for aerospace industry maintenance manuals.
Natural language generation Natural language generation (NLG) is the natural language processing task of generating natural language from a machine representation system such as a knowledge base or a logical form. Psycholinguists prefer the term language production when such formal representations are interpreted as models for mental representations.
Natural Language Toolkit The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It was developed by Steven Bird and Edward Loper in the Department of Computer and Information Science at the University of Pennsylvania. NLTK includes graphical demonstrations and sample data. It is accompanied by a book that explains the underlying concepts behind the language processing tasks supported by the toolkit, plus a cookbook.
Outline of natural language processing Natural language generation – task of converting information from computer databases into readable human language.
Natural language user interface Siri is an intelligent personal assistant application integrated with operating system iOS. The application uses natural language processing to answer questions and make recommendations.