This course is part of the AI in Healthcare Specialization

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Matthew Lungren +1 more instructor
Top Instructors

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Offered By

AI in Healthcare SpecializationStanford University

About this Course

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Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. 
This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.

The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies.

 
Co-author: Geoffrey Angus
 
Contributing Editors:
Mars Huang
Jin Long
Shannon Crawford
Oge Marques


The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.  Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Course 3 of 5 in the

AI in Healthcare Specialization

Beginner Level

Approx. 12 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

What you will learn

Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming.

Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.

Learn important approaches for leveraging data to train, validate, and test machine learning models.

Understand how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 5 in the

AI in Healthcare Specialization

Beginner Level

Approx. 12 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Instructors

Instructor rating

4.91/5 (71 Ratings)

Matthew Lungren
Top Instructor

Associate Professor

Department of Radiology

9,191 Learners

2 Courses

Serena Yeung
Top Instructor

Assistant Professor

Biomedical Data Science

9,191 Learners

2 Courses

Offered by

Stanford University

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

Syllabus - What you will learn from this course

Week

Week 1

2 hours to complete

Why machine learning in healthcare?

2 hours to complete

7 videos (Total 34 min), 6 readings, 3 quizzes

7 videos

Why machine learning in healthcare?3m

History of AI in Medicine6m

Course Overview4m

Why Healthcare Needs Machine Learning2m

Machine Learning Magic8m

Machine Learning, Biostatistics, Programming4m

Can Machine Learning Solve Everything?5m

6 readings

Getting Started: Creators of This Course5m

Video Image Credit

Study Guide Module 15m

Citations and Additional Readings10m

Video Image Credit

3 practice exercises

Reflection Exercise15m

Reflection Exercise10m

Knowledge Check30m

Week

Week 2

2 hours to complete

Concepts and Principles of machine learning in healthcare part 1

2 hours to complete

8 videos (Total 51 min), 2 readings, 3 quizzes

8 videos

Machine Learning Terms, Definitions, and Jargon Part 111m

Machine Learning Terms, Definitions, and Jargon Part 210m

How Machines Learn Part 18m

How Machines Learn Part 25m

Supervised Machine Learning Approaches: Regression and the "No Free Lunch" Theorem3m

Other Traditional Supervised Machine Learning Approaches3m

Support Vector Machine (SVM)3m

Unsupervised Machine Learning4m

2 readings

Study Guide Module 25m

Citations and Additional Readings5m

3 practice exercises

Reflection Exercise10m

Knowledge Check30m

Week

Week 3

2 hours to complete

Concepts and Principles of machine learning in healthcare part 2

2 hours to complete

10 videos (Total 63 min), 3 readings, 3 quizzes

10 videos

Introduction to Deep Learning and Neural Networks8m

Deep Learning and Neural Networks6m

Cross Entropy Loss3m

Gradient Descent6m

Representing Unstructured Image and Text Data5m

Convolutional Neural Networks4m

Natural Language Processing and Recurrent Neural Networks7m

The Transformer Architecture for Sequences4m

Commonly Used and Advanced Neural Network Architectures6m

Advanced Computer Vision Tasks and Wrap-Up10m

3 readings

Video Image Credit

Study Guide Module 35m

Citations and Additional Readings5m

3 practice exercises

Reflection Exercise10m

Knowledge Check30m

Week

Week 4

2 hours to complete

Evaluation and Metrics for machine learning in healthcare

2 hours to complete

5 videos (Total 35 min), 2 readings, 3 quizzes

5 videos

Introduction to Model Performance Evaluation2m

Overfitting and Underfitting8m

Strategies to Address Overfitting, Underfitting and Introduction to Regularization 5m

Statistical Approaches to Model Evaluation5m

Receiver Operator and Precision Recall Curves as Evaluation Metrics11m

2 readings

Study Guide Module 45m

Citations and Additional Readings5m

3 practice exercises

Reflection Exercise 110m

Reflection Exercise 210m

Knowledge Check30m

Reviews

4.8

50 reviews

5 stars
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4 stars
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3 stars
2.79%
2 stars
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TOP REVIEWS FROM FUNDAMENTALS OF MACHINE LEARNING FOR HEALTHCARE

by MNJan 2, 2021

Good course but the language needs to be simpler. Sometimes simple facts are complicated with the use of high pedigree words that don't really add much to conveying the overall message.

by AJSep 8, 2020

Amazing course teaching the innumerous opportunities in the healthcare sector and the application of AI in the same. Beautifully drafted course with intriguing tutorials and exercises.

by LAApr 1, 2021

This was a great course, the presenters really gave a clear view about the differences which could happen when working with health related data set. Very well done,

by LSSep 26, 2020

Interesting and well crafted, it is mostly at an introductory level, but accurate and with many details regarding how to apply ML to healthcare. Worth to follow.

View all reviews

About the AI in Healthcare Specialization

Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses.

In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines. CME Accreditation The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. View the full CME accreditation information on the individual course FAQ page.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Is this activity accredited for Continuing Medical Education (CME)?
Dates and Duration
Original Release Date: 08/10/2020
Expiration Date: 08/10/2023
Estimated Time to Complete: 11 hours
CME Credits Offered: 11.00
Accreditation
The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The Stanford University School of Medicine designates this enduring material for a maximum of 11.00 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Disclosures
The Stanford University School of Medicine adheres to ACCME Criteria, Standards and Policies regarding industry support of continuing medical education. There are no relevant financial relationships with ACCME-defined commercial interests for anyone who was in control of the content of this activity.

More questions? Visit the Learner Help Center.

Fundamentals of Machine Learning for Healthcare