Natural Language Processing in Medicine

Catalog description

This graduate-level course offers an in-depth exploration of Natural Language Processing (NLP) and the application of large language models (LLMs) in the healthcare sector. Students will tackle the complexities of processing unstructured clinical text, medical records, and other health-related documents using advanced NLP techniques. Core topics include text mining, named entity recognition, sentiment analysis, and document classification, with a focus on implementing innovative LLMs like Generative Pre-trained Transformers (GPT) for tasks such as clinical note summarization, patient data extraction, and decision support. Additionally, this course will cover concepts of Generative Adversarial Network (GANs) and diffusion models and their applications in healthcare analytics. The course also addresses ethical concerns, data privacy, and the transformative impact of NLP on patient care and health outcomes. By the end of the course, students will have the expertise to design, develop, and apply NLP models to solve critical healthcare challenges.