Machine Learning in Healthcare: Fundamentals & Applications
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Machine Learning in Healthcare: Fundamentals & Applications
Instructors: Sonya Makhni
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Skills you'll gain
- Diagnostic Tests
- Machine Learning Algorithms
- Clinical Experience
- Machine Learning Software
- AI Integrations
- Machine Learning Methods
- Data Mining
- Deep Learning
- Artificial Intelligence
- Applied Machine Learning
- Random Forest Algorithm
- Model Evaluation
- Unsupervised Learning
- Machine Learning
- Model Training
- Healthcare Industry Knowledge
- Predictive Modeling
- Health Technology
- Responsible AI
- Artificial Intelligence and Machine Learning (AI/ML)
Details to know
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There are 4 modules in this course
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.
In this module, we’ll start demystifying the terminology. We’ll begin by exploring the differences between AI, machine learning and deep learning. You’ll also gain hands-on experience in planning your own AI algorithm development, and learn what goes into preparing and constructing datasets for research questions.
What's included
8 videos7 readings5 assignments1 peer review2 discussion prompts
8 videos•Total 23 minutes
- Meet Your Faculty: Paul Cerrato•1 minute
- Meet Your Faculty: Sonya Makhni•2 minutes
- Module Overview•1 minute
- Defining Data Mining•4 minutes
- Differences Between Machine Learning and Deep Learning•4 minutes
- Linear Regression•2 minutes
- Dataset Construction•5 minutes
- Dataset Preparation•4 minutes
7 readings•Total 73 minutes
- Welcome to Machine Learning in Healthcare: Fundamentals & Applications•1 minute
- Syllabus•10 minutes
- Recommended Prior Knowledge: Basic Statistics•5 minutes
- Recommended Prior Knowledge: How to Read Journal Articles•30 minutes
- Algorithm Project Introduction•5 minutes
- Lesson Resources•20 minutes
- Module Summary•2 minutes
5 assignments•Total 16 minutes
- Module Quiz•9 minutes
- Question to Consider•1 minute
- Check Your Knowledge•3 minutes
- Check Your Knowledge•1 minute
- Check Your Knowledge•2 minutes
1 peer review•Total 120 minutes
- Operational Plan and Dataset for AI Algorithm (Peer Review)•120 minutes
2 discussion prompts•Total 70 minutes
- Welcome to the Course!•10 minutes
- Addressing the 30-Day Readmission Problem•60 minutes
In this module, we’ll take a deep dive into several sophisticated AI modeling techniques, including random forest modeling, gradient boosting, clustering and neural networks.
What's included
6 videos8 readings7 assignments2 discussion prompts
6 videos•Total 19 minutes
- Module Overview•1 minute
- Logistic Regression•3 minutes
- Decision Trees and Random Forest Modeling•4 minutes
- Gradient Boosting•4 minutes
- Clustering•5 minutes
- Neural Networks•3 minutes
8 readings•Total 70 minutes
- Week 2 Project Preview•1 minute
- Lesson Resources•12 minutes
- Week 2 Project Introduction•5 minutes
- Module Summary•2 minutes
- AI Techniques in Clinical Decision Support•7 minutes
- Clustering Study•6 minutes
- Gradient Boosting Study•34 minutes
- AI Explained: What Is A Neural Network?•3 minutes
7 assignments•Total 26 minutes
- Module Quiz•11 minutes
- Honors Quiz•6 minutes
- Question to Consider•1 minute
- Check Your Knowledge•2 minutes
- Check Your Knowledge•2 minutes
- Check Your Knowledge•2 minutes
- Check Your Knowledge•2 minutes
2 discussion prompts•Total 120 minutes
- Can Neural Networks Improve Diagnosis?•60 minutes
- Modeling Technique Selection•60 minutes
In this module, you’ll dive deeper into the nitty gritty of how AI algorithms are trained and validated, and examine how they compare to clinicians in the field.
What's included
6 videos5 readings7 assignments2 discussion prompts
6 videos•Total 16 minutes
- Module Overview•0 minutes
- Applying Data Mining and Machine Learning to Real-World Problems Part 1•4 minutes
- Applying Data Mining and Machine Learning to Real-World Problems Part 2•3 minutes
- Comparing AI Performance to Clinician Performance Part 1•3 minutes
- Analyzing the EAGLE Study•3 minutes
- Comparing AI Performance to Clinician Performance Part 2•4 minutes
5 readings•Total 163 minutes
- Week 3 Project Preview•1 minute
- Study Values: Specificity, Sensitivity, AUC•25 minutes
- Lesson Resources•46 minutes
- Module Summary•1 minute
- Week 3 Project Introduction: The EAGLE Study•90 minutes
7 assignments•Total 31 minutes
- Module Quiz•11 minutes
- Question to Consider•5 minutes
- Check Your Knowledge•3 minutes
- Check Your Knowledge•3 minutes
- Check Your Knowledge•3 minutes
- Check Your Knowledge•3 minutes
- Check Your Knowledge•3 minutes
2 discussion prompts•Total 105 minutes
- Doctors vs. Algorithms•90 minutes
- EAGLE Study•15 minutes
In this module, we’ll 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.
What's included
7 videos6 readings4 assignments2 discussion prompts
7 videos•Total 27 minutes
- Module Overview•1 minute
- Why Clinicians Resist AI-Enabled Algorithms•5 minutes
- Addressing Validation Issues•5 minutes
- Internal/External Validation•4 minutes
- Clinical Validation Studies•2 minutes
- Mayo Clinic on Health AI Part 1•6 minutes
- Mayo Clinic on Health AI Part 2•5 minutes
6 readings•Total 60 minutes
- Week 4 Project Preview•2 minutes
- Lesson Resources•17 minutes
- Lesson Resources•36 minutes
- Week 4 Project Introduction•3 minutes
- Module Summary•1 minute
- Course Summary•1 minute
4 assignments•Total 14 minutes
- Module Quiz•6 minutes
- Check Your Knowledge•3 minutes
- Check Your Knowledge•3 minutes
- Check Your Knowledge•2 minutes
2 discussion prompts•Total 150 minutes
- Healthcare Professionals and AI•60 minutes
- Validation•90 minutes
Instructors
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