SYSEN 5630

Clinical Natural Language Processing and Large-Language Models

Course Description

Course information provided by the Courses of Study 2024-2025.

Natural Language Processing (NLP) is a pivotal technology in artificial intelligence. Its significance has noticeably amplified within the medical field in recent years, as vast amounts of unstructured text data await analysis from databases such as Electronic Medical Records, biomedical literature, and clinical trials. Moreover, the advent of technologies like ChatGPT and other Large Language Models (LLMs) holds the promise of vastly transforming research methodologies and clinical practice. This course aims to provide students comprehensive knowledge of Natural Language Processing, generative AI, and related health applications. Students will learn about various text data sources, integral linguistic structures, and a range of processing methods.

When Offered Spring.

Comments Prerequisite: Python: Prior exposure to programming and Python is highly recommended. We will provide a tutorial on Python in the first two weeks.; Basic Probability and Statistics: You should know the basics of probabilities, mean, standard deviation, etc.; College Calculus, Linear Algebra: You should understand matrix/vector notation and operations.

• Describe different applications of natural language processing in health.
• Identify sources of unstructured data (corpora).
• Analyze unstructured data in terms of linguistic structures.
• Apply pre-processing methods to prepare unstructured data for analysis.
• Define different kinds of structural and statistical features of unstructured data and apply methods for extracting them.

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