Deep Learning for Health Data

3 credits · Allied Health Graduate Program

This course explores deep learning methods and their application to multi-modal and longitudinal health data. Students will learn to design, implement, and evaluate deep neural network architectures for tasks such as medical image analysis, natural language processing of clinical texts, and predictive modeling of patient outcomes from electronic health records. Emphasis is placed on selecting an approach and evaluation strategy that fit the healthcare context. By the end of the course, students will be equipped to develop advanced deep learning models to solve real-world healthcare challenges. Prerequisite(s): BIOSTAT 707 or similar class. Non-program students require permission from the director of graduate studies or instructor. Instructor: Matt Engelhard. Credits: 3