Machine Learning in Healthcare: Fundamentals & Applications
Learn AI and machine learning basics for healthcare. Explore data mining, algorithm development, and real-world applications.
Learn AI and machine learning basics for healthcare. Explore data mining, algorithm development, and real-world applications.
This course examines data mining and machine learning in healthcare contexts. It covers theoretical foundations of major data mining methods, algorithm development, and practical applications. Students learn to select appropriate methods, use data mining software, and develop basic programming skills. The course focuses on solving real-world healthcare problems through data cleaning, transformation, and modeling. It explores AI techniques like random forest modeling, gradient boosting, clustering, and neural networks. Participants gain hands-on experience in planning AI algorithms, preparing datasets, and comparing AI performance to clinicians. The course also addresses challenges in implementing algorithms in healthcare settings.
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What you'll learn
Understand the differences between AI, machine learning, and deep learning in healthcare contexts
Learn to plan AI algorithm development and prepare datasets for healthcare research questions
Gain proficiency in various machine learning techniques including random forest modeling, gradient boosting, and neural networks
Apply data mining and machine learning to real-world healthcare problems
Compare AI algorithm performance to clinician performance in medical diagnosis and decision-making
Understand the challenges and limitations of implementing AI algorithms in healthcare settings
Skills you'll gain
This course includes:
1 Hours PreRecorded video
23 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive introduction to machine learning and artificial intelligence applications in healthcare. It covers fundamental concepts of data mining, AI, and machine learning, including various modeling techniques such as linear regression, logistic regression, decision trees, random forests, gradient boosting, clustering, and neural networks. The curriculum emphasizes practical skills in dataset construction, algorithm development, and performance evaluation. Students learn to apply these techniques to real-world healthcare problems, compare AI performance with clinician performance, and understand the challenges of implementing AI in clinical settings. The course also addresses important issues such as algorithm validation, the "black box" dilemma, and potential biases in healthcare AI.
Demystifying Data Mining and Artificial Intelligence
Module 1 · 5 Hours to complete
Exploring the AI/Machine Learning Toolbox
Module 2 · 3 Hours to complete
Practical Application of AI/Machine Learning
Module 3 · 5 Hours to complete
The Credibility Gap
Module 4 · 4 Hours to complete
Fee Structure
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Financial Aid
Instructors
Instructor at Northeastern University
Sonya Makhni is an instructor at Northeastern University, where she is involved in teaching and developing courses that emphasize practical applications of knowledge in various fields. She has a strong background in her area of expertise and actively contributes to online education through platforms like Coursera. Her courses are designed to enhance learning and provide students with valuable skills applicable in real-world scenarios. For more detailed information about her courses and professional background, you can visit her profile on Coursera.
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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