This course is part of the AI in Healthcare Specialization

4.8
stars
276 ratings

Matthew Lungren +1 more instructor

13,747 already enrolled

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.

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Shareable Certificate

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Course 3 of 5 in the

AI in Healthcare Specialization

Beginner Level

Approx. 12 hours to complete

English

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

Instructors

Instructor rating

4.9/5 (113 Ratings)

Matthew Lungren

Associate Professor

Department of Radiology

15,171 Learners

2 Courses

Serena Yeung

Assistant Professor

Biomedical Data Science

15,171 Learners

2 Courses

Offered by

Stanford University

Syllabus - What you will learn from this course

Content Rating96%(1,427 ratings)

Week1

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?3mHistory of AI in Medicine6mCourse Overview4mWhy Healthcare Needs Machine Learning2mMachine Learning Magic8mMachine Learning, Biostatistics, Programming4mCan Machine Learning Solve Everything?5m

6 readings

Getting Started: Creators of This Course5mVideo Image CreditVideo Image CreditStudy Guide Module 15mCitations and Additional Readings10mVideo Image Credit

3 practice exercises

Reflection Exercise15mReflection Exercise10mKnowledge Check30m

Week2

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 111mMachine Learning Terms, Definitions, and Jargon Part 210mHow Machines Learn Part 18mHow Machines Learn Part 25mSupervised Machine Learning Approaches: Regression and the "No Free Lunch" Theorem3mOther Traditional Supervised Machine Learning Approaches3mSupport Vector Machine (SVM)3mUnsupervised Machine Learning4m

2 readings

Study Guide Module 25mCitations and Additional Readings5m

3 practice exercises

Reflection Exercise10mReflection Exercise10mKnowledge Check30m

Week3

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 Networks8mDeep Learning and Neural Networks6mCross Entropy Loss3mGradient Descent6mRepresenting Unstructured Image and Text Data5mConvolutional Neural Networks4mNatural Language Processing and Recurrent Neural Networks7mThe Transformer Architecture for Sequences4mCommonly Used and Advanced Neural Network Architectures6mAdvanced Computer Vision Tasks and Wrap-Up10m

3 readings

Video Image CreditStudy Guide Module 35mCitations and Additional Readings5m

3 practice exercises

Reflection Exercise10mReflection Exercise10mKnowledge Check30m

Week4

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 Evaluation2mOverfitting and Underfitting8mStrategies to Address Overfitting, Underfitting and Introduction to Regularization 5mStatistical Approaches to Model Evaluation5mReceiver Operator and Precision Recall Curves as Evaluation Metrics11m

2 readings

Study Guide Module 45mCitations and Additional Readings5m

3 practice exercises

Reflection Exercise 110mReflection Exercise 210mKnowledge Check30m

Reviews

4.8

77 reviews

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

by KKFeb 5, 2021

The course was inspiring and useful for a future career! Congratulations to Professor Matthew Lungren and Assistant Professor Serena Yeung! :)

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.

by IIJun 18, 2022

This is the best contents availbale for the new commers to enhance their experties in AI healthcare.

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. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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

About this Course

What you will learn