Clinical Natural Language Processing and Large-Language Models
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.
Topics
- 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.
Official roster learning outcomes
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Clinical Natural Language Processing and Large-Language Models · 3.0 · Graduate