Machine Learning for Healthcare Professionals

Overview

Examines data mining perspectives and methods in a healthcare context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students are exposed to contemporary data mining software applications and basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.

Syllabus

Bridging the AI Gap

In this module, you'll learn about some independent organizations involved in evaluating algorithms, SPIRIT-AI and CONSORT-AI. These are members of the Coalition for Health IT (CHAI). You'll also refresh yourself on the development steps of an AI Algorithm, focusing primarily on where your dataset comes from.

Healthcare Data Analytics

This Module will concentrate on the value of Machine learning in analyzing patient data, including data generated by large clinical trials. You’ll learn about the benefits of subgroup analysis and take a closer look at a large randomized trial conducted by Mayo Clinic investigators. We’ll also explore the difference between correlation and causality, including some unexpected insights about their relationship.

Delivering AI/Machine Learning at the Bedside: Solving the Workflow Problem

In this module we’ll examine the challenge of delivering AI based algorithms in the real world. What works in an AI Lab doesn't always translate at the bedside. With that in mind, we'll discuss the need for creating a delivery system that will help you incorporate these tools into everyday workflows.

The Future of Digital Health

In this module, you’ll gaze into our crystal ball to figure out which AI tools are likely to endure. You’ll take a closer look at conversational tech, generative AI and several Mayo Clinic digital solutions that will transform healthcare in the next few years.