Introduction to Natural Language Processing with Applications to Clinical Data Science
University of Utah
Registration: Use the following link to sign up for this workshop.
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General Information
What: This workshop will introduce methods for using natural language processing (NLP) to extract information from unstructured text data. There will be a primary focus on applications to clinical text and electronic health record notes, but the methods we learn could be applied to other domains.
Who: The course is aimed at graduate students, postdocs, faculty, and other researchers across campus who are interested in learning how to use NLP for data analysis.
Requirements: Participants must bring a laptop on which they can access the internet via a web browser. We will be using Google Colab notebooks throughout this workshop. This workshop will use Python, and students are expected to have an introductory level of Python. If you do not have Python knowledge, we strongly recommend registering for our Introduction to Data Analysis in Python workshop or completing our virtual short course on Python (in development).
Contact: Please email penny.atkins@hsc.utah.edu or alec.chapman@hsc.utah.edu for more information.
Schedule
| Time | Topic | Notebook | Notebook solutions |
|---|---|---|---|
| Before Workshop | Python Review | 1. Python Essentials | |
| 9:00 am | Introduction and Setup | Slides - Intro to NLP | |
| 9:30 am | Working with text in Python | 2. String Methods | 2. String Methods |
| 10:00 am | Regular expressions | 3. Regular Expressions | 3. Regular Expressions |
| 10:30 am | Morning Break | ||
| 10:45 am | Rule-based NLP and medspaCy | 4. Rule-based NLP with medspaCy | 4. Rule-based NLP with medspaCy |
| 11:30 am | Attribute Detection | 5. Attribute Detection | 5. Attribute Detection |
| 12:00 pm | Lunch | ||
| 1:00 pm | Pneumonia classification | 6. Pneumonia Classification | Solutions posted after the workshop |
| 1:45 pm | Introduction to machine learning NLP | Slides - Intro to ML for NLP | |
| 2:00 pm | Pre-training a LM from scratch | 7. Train a language model | |
| 2:30 pm | Afternoon Break | ||
| 2:45 pm | Fine-tuning a LM for classification | 8. Fine tuning text Classification for Pneumonia | |
| 3:30 pm | Wrap-Up and Resources | Slides - ML wrap up and Resources | |
| 4:00 pm | End |