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Laura K. Wiley, PhD
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/ewh57i/clinical
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What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
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.
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.
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.
Practical Application: Identifying Patients with Diabetic Complications
Apply the tools and techniques that you have learned in the course to a real-world example!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops foundational skills in natural language processing, benefiting learners who want to apply NLP techniques to their work
Strong fit for learners in healthcare and data science who need to extract information from clinical notes
Practical application component allows learners to apply techniques to a real-world challenge
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Reviews summary
Nlp for clinical professionals
Learners say this course is well paced and provides a useful approach to Clinical NLP. However, students have complained that the final exams are difficult to grade and have technical glitches. They wish the course included more concepts and note that they had to supplement their learning with additional resources.
The course is well paced and organized.
"Excellent course. Well paced, well thoughtout and put together."
Course provides a useful approach to Clinical NLP.
"Very interesting and useful."
"The course details an approach of NLP which is efficient."
"It may open to other technics used in this field as ML."
The multiple-choice questions in the exam have technical glitches.
"A few glitches in the multiple choice questions during the tests."
This course lacks important NLP concepts.
"Many NLP concepts were left out of this course including ontologies, preferred terms, synonyms, linguistic wildcards, negation etc."
The final exams in this course are difficult to grade.
"the evaluation of the last test to get the certification, I waited for over a month and had to pay two times more in the meantime."
"Don't believe what coursera says ... After completing the course I had to pay another $100 just to wait for the final assignment to be marked to get my certificate."
"With all the time spent and effort to complete this course I found it impossible to be complete."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Clinical Natural Language Processing with these
activities:
Attend a tutorial on using regular expressions
Show steps
Attend a tutorial on using regular expressions for clinical text processing to improve your proficiency.
Browse courses on
Regular Expressions
Show steps
-
Find a tutorial on using regular expressions for clinical text processing.
-
Follow the steps in the tutorial to learn how to use regular expressions.
-
Practice using regular expressions on sample clinical text.
Join a study group to discuss course material
Show steps
Join a study group to discuss course material, share insights, and collaborate on assignments.
Show steps
-
Find a study group or create one with classmates.
-
Set regular meeting times and stick to them.
-
Share notes, discuss course readings, and work on assignments together.
Develop a text processing algorithm to identify diabetic complications
Show steps
Develop a text processing algorithm to identify diabetic complications which will enhance your problem-solving and analytical abilities.
Browse courses on
Clinical Decision Support
Show steps
-
Gather a dataset of clinical notes containing information on diabetic complications.
-
Explore the dataset and identify patterns and features associated with diabetic complications.
-
Design and implement a text processing algorithm to extract relevant information from the clinical notes.
-
Evaluate the performance of the algorithm using metrics such as accuracy, recall, and precision.
-
Write a report summarizing the algorithm's performance and potential applications in clinical settings.
One other activity
Expand to see all activities and additional details
Show all four activities
Create a comprehensive study guide
Show steps
Compile a comprehensive study guide to improve your retention and overall understanding of the concepts
Show steps
-
Gather notes, assignments, and other relevant materials from the course.
-
Review the materials and identify key concepts.
-
Organize the materials into a logical and structured format.
-
Include summaries, diagrams, and practice questions to reinforce your understanding.
Attend a tutorial on using regular expressions
Show steps
Attend a tutorial on using regular expressions for clinical text processing to improve your proficiency.
Browse courses on
Regular Expressions
Show steps
Show steps
Show steps
- Find a tutorial on using regular expressions for clinical text processing.
- Follow the steps in the tutorial to learn how to use regular expressions.
- Practice using regular expressions on sample clinical text.
Join a study group to discuss course material
Show steps
Join a study group to discuss course material, share insights, and collaborate on assignments.
Show steps
Show steps
Show steps
- Find a study group or create one with classmates.
- Set regular meeting times and stick to them.
- Share notes, discuss course readings, and work on assignments together.
Develop a text processing algorithm to identify diabetic complications
Show steps
Develop a text processing algorithm to identify diabetic complications which will enhance your problem-solving and analytical abilities.
Browse courses on
Clinical Decision Support
Show steps
Show steps
Show steps
- Gather a dataset of clinical notes containing information on diabetic complications.
- Explore the dataset and identify patterns and features associated with diabetic complications.
- Design and implement a text processing algorithm to extract relevant information from the clinical notes.
- Evaluate the performance of the algorithm using metrics such as accuracy, recall, and precision.
- Write a report summarizing the algorithm's performance and potential applications in clinical settings.
One other activity
Expand to see all activities and additional details
Show all four activities
Create a comprehensive study guide
Show steps
Compile a comprehensive study guide to improve your retention and overall understanding of the concepts
Show steps
Show steps
Show steps
- Gather notes, assignments, and other relevant materials from the course.
- Review the materials and identify key concepts.
- Organize the materials into a logical and structured format.
- Include summaries, diagrams, and practice questions to reinforce your understanding.
Career center
Learners who complete Clinical Natural Language Processing will develop knowledge and skills
that may be useful to these careers:
Health Data Analyst
Health Data Analyst
A Health Data Analyst helps to analyze and interpret clinical data to identify trends and patterns that can be used to improve patient care. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for working with clinical data. By taking this course, you will be well-prepared for a career as a Health Data Analyst.
Clinical Research Associate
Clinical Research Associate
A Clinical Research Associate helps to design and conduct clinical trials to test new drugs and treatments. This course provides a strong foundation in clinical natural language processing, which is essential for understanding and interpreting clinical data. By taking this course, you will be well-prepared for a career as a Clinical Research Associate.
Medical Writer
Medical Writer
A Medical Writer helps to create clear and accurate medical documents, such as patient education materials, clinical trial protocols, and scientific manuscripts. This course provides a strong foundation in clinical natural language processing, which is essential for understanding and interpreting clinical data. By taking this course, you will be well-prepared for a career as a Medical Writer.
Health Informatics Specialist
Health Informatics Specialist
A Health Informatics Specialist helps to develop and implement health information systems, such as electronic health records and clinical decision support tools. This course provides a strong foundation in clinical natural language processing, which is essential for understanding and interpreting clinical data. By taking this course, you will be well-prepared for a career as a Health Informatics Specialist.
Data Scientist
Data Scientist
A Data Scientist helps to analyze and interpret data to identify trends and patterns that can be used to inform decision-making. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for working with data. By taking this course, you will be well-prepared for a career as a Data Scientist.
Software Engineer
Software Engineer
A Software Engineer helps to design, develop, and maintain software applications. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for working with software applications. By taking this course, you will be well-prepared for a career as a Software Engineer.
Information Architect
Information Architect
An Information Architect helps to design and organize information systems, such as websites and databases. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for working with information systems. By taking this course, you will be well-prepared for a career as an Information Architect.
User Experience Designer
User Experience Designer
A User Experience Designer helps to design and develop user-friendly interfaces for software applications and websites. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for understanding and interpreting user needs. By taking this course, you will be well-prepared for a career as a User Experience Designer.
Content Strategist
Content Strategist
A Content Strategist helps to develop and implement content strategies for websites, social media, and other digital platforms. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for understanding and interpreting user needs. By taking this course, you will be well-prepared for a career as a Content Strategist.
Technical Writer
Technical Writer
A Technical Writer helps to create clear and accurate technical documentation, such as user manuals, white papers, and training materials. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for understanding and interpreting technical information. By taking this course, you will be well-prepared for a career as a Technical Writer.
Technical Communicator
Technical Communicator
A Technical Communicator helps to communicate technical information to a variety of audiences, such as customers, employees, and partners. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for understanding and interpreting technical information. By taking this course, you will be well-prepared for a career as a Technical Communicator.
Science Writer
Science Writer
A Science Writer helps to communicate scientific information to a variety of audiences, such as the general public, students, and researchers. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for understanding and interpreting scientific information. By taking this course, you will be well-prepared for a career as a Science Writer.
Science Communicator
Science Communicator
A Science Communicator helps to communicate scientific information to a variety of audiences, such as the general public, students, and researchers. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for understanding and interpreting scientific information. By taking this course, you will be well-prepared for a career as a Science Communicator.
Science Journalist
Science Journalist
A Science Journalist helps to communicate scientific information to the general public. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for understanding and interpreting scientific information. By taking this course, you will be well-prepared for a career as a Science Journalist.
Research Scientist
Research Scientist
A Research Scientist helps to conduct research in a variety of fields, such as biology, chemistry, and physics. This course provides a strong foundation in text mining, text processing, and natural language processing, which are essential skills for understanding and interpreting scientific information. By taking this course, you may be better prepared for a career as a Research Scientist.
For more career information including salaries, visit:
OpenCourser.com/course/ewh57i/clinical
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Clinical Natural Language Processing.
Provides a comprehensive overview of natural language processing techniques, including text mining, text processing, and natural language processing. It also covers the linguistic foundations that underly NLP tools.
Provides a comprehensive overview of deep learning for natural language processing, including how to use deep learning to develop text processing algorithms. It also includes a number of case studies of how deep learning has been used to improve healthcare.
Provides a comprehensive overview of artificial intelligence in healthcare, including how to use AI to develop text processing algorithms. It also includes a number of case studies of how AI has been used to improve healthcare.
Save
Provides a practical guide to text mining with R, including how to extract information from clinical notes. It also includes a number of case studies of how text mining has been used to improve healthcare.
Provides a comprehensive guide to regular expressions, including how to write regular expressions and handle text data in R. It also includes a number of practical examples of how to use regular expressions to process clinical text.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a popular open-source library for natural language processing. It includes a number of tutorials and case studies on how to use NLTK to process clinical text.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ewh57i/clinical
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Effort
5 weeks of study, 2-4 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Colorado System
Instructor
Laura K. Wiley, PhD
Language
English
Transcripts
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This course belongs to a
collection
called
Clinical Data Science. It's comprised of six courses:
- Introduction to Clinical Data Science
- Clinical Natural Language Processing (viewing)
- Clinical Data Models and Data Quality Assessments
- Predictive Modeling and Transforming Clinical Practice
- Identifying Patient Populations
- Advanced Clinical Data Science
Good to know
Develops foundational skills in natural language processing, benefiting learners who want to apply NLP techniques to their work
Strong fit for learners in healthcare and data science who need to extract information from clinical notes
Practical application component allows learners to apply techniques to a real-world challenge
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