Advanced AI: NLP Techniques for Clinical Datasets

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Wuraola Oyewusi

43:49

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01 - Introduction

01 - Use NLP techniques for your data.mp4

00:45

02 - What you should know.mp4

00:17

03 - How to use the exercise files.mp4

01:25

02 - 1. Clinical Named Entity Recognition (CNER)

01 - What is clinical named entity recognition (CNER).mp4

04:21

02 - Clinical named entity recognition using scispaCy.mp4

05:01

03 - 2. Clinical Entity Resolution

01 - What is clinical entity resolution.mp4

03:32

02 - Medical abbreviation resolution with scispaCy.mp4

02:31

03 - Entity linkage and resolution with a biomedical knowledge base.mp4

04:19

04 - 3. Clinical Text Representation

01 - What is clinical text representation.mp4

02:38

02 - Clinical text representation using fastText.mp4

06:02

03 - Clinical text representation using Universal Sentence Encoder (USE).mp4

02:22

05 - 4. Transformers for Clinical Text

01 - What are transformers.mp4

02:31

02 - Clinical diagnosis prediction using transformers.mp4

02:54

03 - Clinical named entity recognition using transformers.mp4

02:09

04 - Clinical word prediction using transformers.mp4

02:34

06 - Conclusion

01 - Next steps.mp4

00:28

Description

The healthcare industry is one of the fastest growing sectors using AI applications and techniques. When working with clinical data, written text forms a major part of how scenarios and treatment progression are documented. With the advent and availability of more and more digital health data, this course provides hands-on lessons at making sense of clinical text data using natural language processing (NLP) techniques. Join instructor Wuraola Oyewusi as she explores how to apply natural language processing to clinical and biomedical data. Topics covered include clinical named entity recognition, clinical entity resolution, word and sentence level text representation, and transformers for clinical text.

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Artificial Intelligence

Natural Language Processing

Wuraola Oyewusi

Instructor's Courses

Experienced data scientist (DS), machine learning (ML), and artificial intelligence (AI) professional with expertise in natural language processing (NLP), healthcare, data curation, and research. Recognized as a UK Global Talent in AI, Machine Learning, and Data Science

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.