Machine Learning in Healthcare: Fundamentals & Applications

Machine Learning in Healthcare: Fundamentals & Applications

Instructors: Sonya Makhni

Access provided by ExxonMobil

1,717 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

Beginner level

No prior experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

Beginner level

No prior experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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Assessments

23 assignments

Taught in English

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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 videosTotal 23 minutes

Meet Your Faculty: Paul Cerrato1 minute
Meet Your Faculty: Sonya Makhni2 minutes
Module Overview1 minute
Defining Data Mining4 minutes
Differences Between Machine Learning and Deep Learning4 minutes
Linear Regression2 minutes
Dataset Construction5 minutes
Dataset Preparation4 minutes

7 readingsTotal 73 minutes

Welcome to Machine Learning in Healthcare: Fundamentals & Applications1 minute
Syllabus10 minutes
Recommended Prior Knowledge: Basic Statistics5 minutes
Recommended Prior Knowledge: How to Read Journal Articles30 minutes
Algorithm Project Introduction5 minutes
Lesson Resources20 minutes
Module Summary2 minutes

5 assignmentsTotal 16 minutes

Question to Consider1 minute
Check Your Knowledge3 minutes
Check Your Knowledge1 minute
Check Your Knowledge2 minutes
Module Quiz9 minutes

1 peer reviewTotal 120 minutes

Operational Plan and Dataset for AI Algorithm (Peer Review)120 minutes

2 discussion promptsTotal 70 minutes

Welcome to the Course!10 minutes
Addressing the 30-Day Readmission Problem60 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 videosTotal 19 minutes

Module Overview1 minute
Logistic Regression3 minutes
Decision Trees and Random Forest Modeling4 minutes
Gradient Boosting4 minutes
Clustering5 minutes
Neural Networks3 minutes

8 readingsTotal 70 minutes

Week 2 Project Preview1 minute
Lesson Resources12 minutes
Week 2 Project Introduction5 minutes
Module Summary2 minutes
AI Techniques in Clinical Decision Support7 minutes
Clustering Study6 minutes
Gradient Boosting Study34 minutes
AI Explained: What Is A Neural Network?3 minutes

7 assignmentsTotal 26 minutes

Question to Consider1 minute
Check Your Knowledge2 minutes
Check Your Knowledge2 minutes
Check Your Knowledge2 minutes
Check Your Knowledge2 minutes
Module Quiz11 minutes
Honors Quiz6 minutes

2 discussion promptsTotal 120 minutes

Can Neural Networks Improve Diagnosis?60 minutes
Modeling Technique Selection60 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 videosTotal 16 minutes

Module Overview0 minutes
Applying Data Mining and Machine Learning to Real-World Problems Part 14 minutes
Applying Data Mining and Machine Learning to Real-World Problems Part 23 minutes
Comparing AI Performance to Clinician Performance Part 13 minutes
Analyzing the EAGLE Study3 minutes
Comparing AI Performance to Clinician Performance Part 24 minutes

5 readingsTotal 163 minutes

Week 3 Project Preview1 minute
Study Values: Specificity, Sensitivity, AUC25 minutes
Lesson Resources46 minutes
Module Summary1 minute
Week 3 Project Introduction: The EAGLE Study90 minutes

7 assignmentsTotal 31 minutes

Question to Consider5 minutes
Check Your Knowledge3 minutes
Check Your Knowledge3 minutes
Check Your Knowledge3 minutes
Check Your Knowledge3 minutes
Check Your Knowledge3 minutes
Module Quiz11 minutes

2 discussion promptsTotal 105 minutes

Doctors vs. Algorithms90 minutes
EAGLE Study15 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.