04-801-T3   Applications of AI in Healthcare

Course description

This course introduces the principles and practices underlying the application of AI in healthcare. Students will survey the opportunities and challenges of using AI to improve healthcare, particularly in Africa. Students will study the potential impact of AI on broad groups of users including patients, providers, and health-related industries via domains such as medical image analysis, electronic health records (EHR), and customized drugs. Topics include, but are not limited, to neural networks and deep learning models for multimodal analysis as well as the use of time-series data. Practical questions around privacy and inclusiveness, and technical challenges of integration with existing systems will also be presented. Students will be assessed on weekly readings and homework assignments.

Learning objectives

The main objectives of this course are to:

• Distinguish the main domains within healthcare and become familiar with the types of data and machine learning algorithms used therein
• Explore clinical topics incl. risk classification, modeling of disease progression, and drug customization
• Explore mathematical topics incl. graphical models, time-series analysis, causality
• Understand the significance of related topics such as workflow design, algorithmic fairness

Outcomes

Upon successful completion of this course, students should be able to:

• Start working with healthcare data
• Translate healthcare problems to machine learning problems
• Select appropriate learning algorithms
• Recognize limits and opportunities in applying machine learning (ML) to healthcare

Content details

• Introduction to clinical data and EHR
• Monitoring patients
• Signal prediction
• ML techniques commonly used in healthcare
• Time-varying and multi-trait data
• Regulation and governance

Prerequisites

None. Open to all CMU-Africa students with a math and programming background in IT, engineering, or AI.

Faculty

Ahmed Biyabani