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Stanford University

Fundamentals of Machine Learning for Healthcare

Stanford University via Coursera

Overview

Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles.

This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.

The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies.


Co-author: Geoffrey Angus

Contributing Editors:
Mars Huang
Jin Long
Shannon Crawford
Oge Marques


The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Syllabus

  • Why machine learning in healthcare?
  • Concepts and Principles of machine learning in healthcare part 1
  • Concepts and Principles of machine learning in healthcare part 2
  • Evaluation and Metrics for machine learning in healthcare
  • Strategies and Challenges in Machine Learning in Healthcare
  • Best practices, teams, and launching your machine learning journey
  • Course Conclusion

Taught by

Matthew Lungren and Serena Yeung

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