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.
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About this Course
최근 조회 7,745회
배울 내용
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.
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 6개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 21시간 필요
권장: 5 weeks of study, 2-4 hours/week
영어
자막: 영어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 1시간 필요
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.
완료하는 데 1시간 필요
7개 동영상 (총 20분), 3 readings, 1 quiz
7개의 동영상
Introduction to Clinical Natural Language Processing3m
NLP Fundamentals: Linguistics1m
NLP Fundamentals: Morphology & Lexicography4m
NLP Fundamentals: Syntax4m
NLP Fundamentals: Sematics & Pragmatics4m
NLP Fundamentals: Wrap Up40
3개의 읽기 자료
Introduction to Specialization Instructors5m
Course Policies5m
Accessing Course Data and Technology Platform15m
1개 연습문제
Week 1 Assessment20m
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
3개 동영상 (총 17분), 2 readings, 2 quizzes
3개의 동영상
Text Processing in the Tidyverse3m
Tips and Tricks for Text Processing5m
2개의 읽기 자료
Regular Expressions and Text Processing in R1h
Note about the Assessment2m
2개 연습문제
Regular Expressions and Text Processing in R - Try it Out For Yourself Exercises40m
Week 2 Assessment30m
완료하는 데 3시간 필요
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.
완료하는 데 3시간 필요
4개 동영상 (총 14분), 2 readings, 2 quizzes
4개의 동영상
Clinical Note Types: History and Physical Notes2m
Clinical Note Types: Discharge Summaries2m
Clinical Note Types: Radiology Reports2m
2개의 읽기 자료
Note Section Techniques1시 30분
Note about the Assessment2m
2개 연습문제
Note Section Techniques - Try It Out For Yourself Excercises45m
Week 3 Assessment30m
완료하는 데 3시간 필요
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.
완료하는 데 3시간 필요
1개 동영상 (총 5분), 2 readings, 2 quizzes
1개의 동영상
2개의 읽기 자료
Keyword Windows Techniques2h
Note about the Assessment2m
2개 연습문제
Keyword Windows Techniques - Try it Out For Yourself Answers45m
Week 4 Assessment30m
강사
Laura K. Wiley, PhD
Assistant ProfessorDivision of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus
콜로라도 대학교 정보
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.
Clinical Data Science 전문 분야 정보
Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist!
This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data.
By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data.
This specialization is supported by our industry partnership with Google Cloud. Thanks to this support, all learners will have access to a fully hosted online data science computational environment for free! Please note that you must have access to a Google account (i.e., gmail account) to access the clinical data and computational environment.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
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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