Machine Learning in Healthcare: Fundamentals & Applications
- Offered byCoursera
Machine Learning in Healthcare: Fundamentals & Applications at Coursera Overview
Duration | 18 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course |
Credential | Certificate |
Machine Learning in Healthcare: Fundamentals & Applications at Coursera Highlights
- Earn a certificate from Northeastern University
- Add to your LinkedIn profile
- 23 quizzes
Machine Learning in Healthcare: Fundamentals & Applications at Coursera Course details
- 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
Machine Learning in Healthcare: Fundamentals & Applications at Coursera Curriculum
Demystifying Data Mining and Artificial Intelligence
Meet Your Faculty: Paul Cerrato
Meet Your Faculty: Sonya Makhni
Module Overview
Defining Data Mining
Differences Between Machine Learning and Deep Learning
Linear Regression
Dataset Construction
Dataset Preparation
Welcome to Machine Learning in Healthcare: Fundamentals & Applications
Syllabus
Recommended Prior Knowledge: Basic Statistics
Recommended Prior Knowledge: How to Read Journal Articles
Algorithm Project Introduction
Lesson Resources
Module Summary
Question to Consider
Check Your Knowledge
Check Your Knowledge
Check Your Knowledge
Module Quiz
Operational Plan and Dataset for AI Algorithm (Peer Review)
Welcome to the Course!
Addressing the 30-Day Readmission Problem
Exploring the AI/Machine Learning Toolbox
Module Overview
Logistic Regression
Decision Trees and Random Forest Modeling
Gradient Boosting
Clustering
Neural Networks
Week 2 Project Preview
Lesson Resources
Week 2 Project Introduction
Module Summary
AI Techniques in Clinical Decision Support
Clustering Study
Gradient Boosting Study
AI Explained: What Is A Neural Network?
Question to Consider
Check Your Knowledge
Check Your Knowledge
Check Your Knowledge
Check Your Knowledge
Module Quiz
Honors Quiz
Can Neural Networks Improve Diagnosis?
Modeling Technique Selection
Practical Application of AI/Machine Learning
Module Overview
Applying Data Mining and Machine Learning to Real-World Problems Part 1
Applying Data Mining and Machine Learning to Real-World Problems Part 2
Comparing AI Performance to Clinician Performance Part 1
Analyzing the EAGLE Study
Comparing AI Performance to Clinician Performance Part 2
Week 3 Project Preview
Study Values: Specificity, Sensitivity, AUC
Lesson Resources
Module Summary
Week 3 Project Introduction: The EAGLE Study
Question to Consider
Check Your Knowledge
Check Your Knowledge
Check Your Knowledge
Check Your Knowledge
Check Your Knowledge
Module Quiz
Doctors vs. Algorithms
EAGLE Study
The Credibility Gap
Module Overview
Why Clinicians Resist AI-Enabled Algorithms
Addressing Validation Issues
Internal/External Validation
Clinical Validation Studies
Mayo Clinic on Health AI Part 1
Mayo Clinic on Health AI Part 2
Week 4 Project Preview
Lesson Resources
Lesson Resources
Week 4 Project Introduction
Module Summary
Course Summary
Check Your Knowledge
Check Your Knowledge
Check Your Knowledge
Module Quiz
Healthcare Professionals and AI
Validation