Machine Learning in Healthcare: Fundamentals & Applications

Northeastern University via Coursera

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Overview

"Machine Learning in Healthcare: Fundamentals & Applications" is a specialized course designed to introduce learners to the foundational principles and real-world applications of machine learning (ML) within the healthcare industry. This course is ideal for healthcare professionals, data scientists, and AI enthusiasts interested in improving patient outcomes and operational efficiency through advanced analytics. The course covers core ML concepts such as supervised and unsupervised learning, model evaluation, and feature selection, with a focus on healthcare-specific data challenges like electronic health records...

Syllabus

Demystifying Data Mining and Artificial Intelligence

In this module, well start demystifying the terminology. Well begin by exploring the differences between AI, machine learning and deep learning. Youll also gain hands-on experience in planning your own AI algorithm development, and learn what goes into preparing and constructing datasets for research questions.

Exploring the AI/Machine Learning Toolbox

In this module, well take a deep dive into several sophisticated AI modeling techniques, including random forest modeling, gradient boosting, clustering and neural networks.

Practical Application of AI/Machine Learning

In this module, youll dive deeper into the nitty gritty of how AI algorithms are trained and validated, and examine how they compare to clinicians in the field.

The Credibility Gap

In this module, well explore why so many potentially useful algorithms are not being implemented by healthcare providers. That critique will explore the black box dilemma, and the challenges involved in developing accurate and equitable data sets. That means examining the many ways in which algorithms can discriminate against various marginalized segments of the population.