Machine Learning for Healthcare Professionals
Learn AI in healthcare: datasets, algorithms, and real-world implementation for improved patient care.
Learn AI in healthcare: datasets, algorithms, and real-world implementation for improved patient care.
Dive into the world of machine learning in healthcare with this comprehensive course. Designed for healthcare professionals, it covers essential topics from data mining and algorithm evaluation to real-world implementation. Learn about dataset construction, subgroup analysis, and the challenges of delivering AI at the bedside. Explore the future of digital health, including conversational tech and generative AI. Gain practical skills in analyzing patient data, understanding AI limitations, and navigating regulatory impacts. Perfect for those looking to bridge the gap between healthcare and AI.
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What you'll learn
Understand the development steps of AI algorithms in healthcare
Learn about dataset construction and preparation for healthcare applications
Explore the benefits and risks of using large language models in healthcare
Understand the challenges of delivering AI-based algorithms at the bedside
Learn about conversational tech tools and generative AI in healthcare
Explore Mayo Clinic digital solutions and their impact on future healthcare
Skills you'll gain
This course includes:
64 Minutes PreRecorded video
17 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course examines data mining perspectives and methods in a healthcare context, introducing theoretical foundations for major data mining methods and their appropriate selection. Students will learn about dataset construction, subgroup analysis, and the challenges of implementing AI algorithms in real-world healthcare settings. The course covers the evaluation of AI algorithms, the impact of governmental regulations, and explores future trends in digital health, including conversational tech and generative AI. Practical skills in analyzing patient data and understanding the limitations of AI in healthcare are emphasized throughout the modules.
Bridging the AI Gap
Module 1 · 5 Hours to complete
Healthcare Data Analytics
Module 2 · 2 Hours to complete
Delivering AI/Machine Learning at the Bedside: Solving the Workflow Problem
Module 3 · 2 Hours to complete
The Future of Digital Health
Module 4 · 2 Hours to complete
Fee Structure
Payment options
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Instructor
Bridging Healthcare and Technology through Education and Research
Paul Cerrato is a Visiting Lecturer at Northeastern University’s D'Amore-McKim School of Business, where he teaches courses on data mining and machine learning, focusing on their applications in healthcare. With over 30 years of experience as a research analyst, medical journalist, and educator, Cerrato has made significant contributions to the understanding of clinical decision support systems, electronic health records, and health information security. His role as a Senior Research Analyst at the Mayo Clinic Platform allows him to explore innovative solutions in clinical data analytics and telemedicine. Cerrato has co-authored several influential books that discuss the intersection of healthcare and technology, including topics on artificial intelligence and mobile health. His extensive writing portfolio includes contributions to leading healthcare publications and recognition as one of the most influential voices in healthcare IT by HIMSS. Through his teaching and research, Cerrato continues to advocate for evidence-based practices that enhance patient care and improve healthcare delivery systems.
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