本课程是 AI in Healthcare 专项课程专项课程的一部分

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Matthew Lungren 另外 +1 位授课教师

13,154 人已注册

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AI in Healthcare 专项课程斯坦福大学

课程信息

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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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第 3 门课程（共 5 门）

AI in Healthcare 专项课程

初级

完成时间大约为12 小时

英语（English）

您将学到的内容有

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.

可灵活调整截止日期

根据您的日程表重置截止日期。

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

第 3 门课程（共 5 门）

AI in Healthcare 专项课程

初级

完成时间大约为12 小时

英语（English）

授课教师

授课教师评分

4.9/5 （111 个评分）

Matthew Lungren

Associate Professor

Department of Radiology

14,508 个学生

2 门课程

Serena Yeung

Assistant Professor

Biomedical Data Science

14,508 个学生

2 门课程

提供方

斯坦福大学

授课大纲 - 您将从这门课程中学到什么

内容评分96%（1,335 个评分）

周

周 1

完成时间为 2 小时

Why machine learning in healthcare?

完成时间为 2 小时

7 个视频（总计 34 分钟）, 6 个阅读材料, 3 个测验

7 个视频

Why machine learning in healthcare?3分钟

History of AI in Medicine6分钟

Course Overview4分钟

Why Healthcare Needs Machine Learning2分钟

Machine Learning Magic8分钟

Machine Learning, Biostatistics, Programming4分钟

Can Machine Learning Solve Everything?5分钟

6 个阅读材料

Getting Started: Creators of This Course5分钟

Video Image Credit

Study Guide Module 15分钟

Citations and Additional Readings10分钟

Video Image Credit

3 个练习

Reflection Exercise15分钟

Reflection Exercise10分钟

Knowledge Check30分钟

周

周 2

完成时间为 2 小时

Concepts and Principles of machine learning in healthcare part 1

完成时间为 2 小时

8 个视频（总计 51 分钟）, 2 个阅读材料, 3 个测验

8 个视频

Machine Learning Terms, Definitions, and Jargon Part 111分钟

Machine Learning Terms, Definitions, and Jargon Part 210分钟

How Machines Learn Part 18分钟

How Machines Learn Part 25分钟

Supervised Machine Learning Approaches: Regression and the "No Free Lunch" Theorem3分钟

Other Traditional Supervised Machine Learning Approaches3分钟

Support Vector Machine (SVM)3分钟

Unsupervised Machine Learning4分钟

2 个阅读材料

Study Guide Module 25分钟

Citations and Additional Readings5分钟

3 个练习

Reflection Exercise10分钟

Knowledge Check30分钟

周

周 3

完成时间为 2 小时

Concepts and Principles of machine learning in healthcare part 2

完成时间为 2 小时

10 个视频（总计 63 分钟）, 3 个阅读材料, 3 个测验

10 个视频

Introduction to Deep Learning and Neural Networks8分钟

Deep Learning and Neural Networks6分钟

Cross Entropy Loss3分钟

Gradient Descent6分钟

Representing Unstructured Image and Text Data5分钟

Convolutional Neural Networks4分钟

Natural Language Processing and Recurrent Neural Networks7分钟

The Transformer Architecture for Sequences4分钟

Commonly Used and Advanced Neural Network Architectures6分钟

Advanced Computer Vision Tasks and Wrap-Up10分钟

3 个阅读材料

Video Image Credit

Study Guide Module 35分钟

Citations and Additional Readings5分钟

3 个练习

Reflection Exercise10分钟

Knowledge Check30分钟

周

周 4

完成时间为 2 小时

Evaluation and Metrics for machine learning in healthcare

完成时间为 2 小时

5 个视频（总计 35 分钟）, 2 个阅读材料, 3 个测验

5 个视频

Introduction to Model Performance Evaluation2分钟

Overfitting and Underfitting8分钟

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

Statistical Approaches to Model Evaluation5分钟

Receiver Operator and Precision Recall Curves as Evaluation Metrics11分钟

2 个阅读材料

Study Guide Module 45分钟

Citations and Additional Readings5分钟

3 个练习

Reflection Exercise 110分钟

Reflection Exercise 210分钟

Knowledge Check30分钟

审阅

4.8

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5 stars
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2 stars
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来自FUNDAMENTALS OF MACHINE LEARNING FOR HEALTHCARE的热门评论

由 HJ 提供Oct 4, 2021

it is a really good course for learning ML but some of the videos are a bit hard to fully understand

由 BH 提供Aug 31, 2022

the was very interesting course ,I share a lot of experience and knowledge form the course thank you

由 KS 提供Oct 16, 2020

Nicely Framed and Executed in a simple language so anyone can catch up earliest.

由 CD 提供Mar 4, 2021

Excellent introductory course to understand Machine Learning in the context of Healthcare delivery

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关于 AI in Healthcare 专项课程

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.

常见问题

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讲座和作业的访问权限取决于您的注册类型。如果您以旁听模式参加课程，则可以免费查看大多数课程资料。要访问评分作业并获得证书，您需要在旁听期间或之后购买证书体验。如果看不到旁听选项：
课程可能不提供旁听选项。您可以尝试免费试用，也可以申请助学金。
课程可能会改为提供'完整课程，没有证书'。通过此选项，您可以查看所有课程材料、提交所要求的作业，以及获得最终成绩。这也意味着您将无法购买证书体验。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
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是的。如果您负担不起学习计划的注册费，则可以在选择学习计划时申请助学金或奖学金。如果您选择的学习计划提供助学金或奖学金，则您将在描述页面上看到一个申请链接。
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.

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Fundamentals of Machine Learning for Healthcare

课程信息

您将学到的内容有