Este curso faz parte do Programa de cursos integrados AI in Healthcare

4.8
estrelas
268 classificações

Matthew Lungren +1 mais instrutores

13.047 já se inscreveram

oferecido por

Programa de cursos integrados AI in HealthcareUniversidade de Stanford

Informações sobre o curso

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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.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Curso 3 de 5 no

Programa de cursos integrados AI in Healthcare

Nível iniciante

Aprox. 12 horas para completar

Inglês

O que você vai aprender

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.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Curso 3 de 5 no

Programa de cursos integrados AI in Healthcare

Nível iniciante

Aprox. 12 horas para completar

Inglês

Instrutores

Classificação do instrutor

4,9/5 (111 classificações)

Matthew Lungren

Associate Professor

Department of Radiology

14.386 aprendizes

2 Cursos

Serena Yeung

Assistant Professor

Biomedical Data Science

14.386 aprendizes

2 Cursos

oferecido por

Universidade de Stanford

Programa - O que você aprenderá com este curso

Classificação do conteúdo96%(1,334 classificações)

Semana

Semana 1

2 horas para concluir

Why machine learning in healthcare?

2 horas para concluir

7 vídeos (Total 34 mín.), 6 leituras, 3 testes

7 videos

Why machine learning in healthcare?3min

History of AI in Medicine6min

Course Overview4min

Why Healthcare Needs Machine Learning2min

Machine Learning Magic8min

Machine Learning, Biostatistics, Programming4min

Can Machine Learning Solve Everything?5min

6 leituras

Getting Started: Creators of This Course5min

Video Image Credit

Study Guide Module 15min

Citations and Additional Readings10min

Video Image Credit

3 exercícios práticos

Reflection Exercise15min

Reflection Exercise10min

Knowledge Check30min

Semana

Semana 2

2 horas para concluir

Concepts and Principles of machine learning in healthcare part 1

2 horas para concluir

8 vídeos (Total 51 mín.), 2 leituras, 3 testes

8 videos

Machine Learning Terms, Definitions, and Jargon Part 111min

Machine Learning Terms, Definitions, and Jargon Part 210min

How Machines Learn Part 18min

How Machines Learn Part 25min

Supervised Machine Learning Approaches: Regression and the "No Free Lunch" Theorem3min

Other Traditional Supervised Machine Learning Approaches3min

Support Vector Machine (SVM)3min

Unsupervised Machine Learning4min

2 leituras

Study Guide Module 25min

Citations and Additional Readings5min

3 exercícios práticos

Reflection Exercise10min

Knowledge Check30min

Semana

Semana 3

2 horas para concluir

Concepts and Principles of machine learning in healthcare part 2

2 horas para concluir

10 vídeos (Total 63 mín.), 3 leituras, 3 testes

10 videos

Introduction to Deep Learning and Neural Networks8min

Deep Learning and Neural Networks6min

Cross Entropy Loss3min

Gradient Descent6min

Representing Unstructured Image and Text Data5min

Convolutional Neural Networks4min

Natural Language Processing and Recurrent Neural Networks7min

The Transformer Architecture for Sequences4min

Commonly Used and Advanced Neural Network Architectures6min

Advanced Computer Vision Tasks and Wrap-Up10min

3 leituras

Video Image Credit

Study Guide Module 35min

Citations and Additional Readings5min

3 exercícios práticos

Reflection Exercise10min

Knowledge Check30min

Semana

Semana 4

2 horas para concluir

Evaluation and Metrics for machine learning in healthcare

2 horas para concluir

5 vídeos (Total 35 mín.), 2 leituras, 3 testes

5 videos

Introduction to Model Performance Evaluation2min

Overfitting and Underfitting8min

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

Statistical Approaches to Model Evaluation5min

Receiver Operator and Precision Recall Curves as Evaluation Metrics11min

2 leituras

Study Guide Module 45min

Citations and Additional Readings5min

3 exercícios práticos

Reflection Exercise 110min

Reflection Exercise 210min

Knowledge Check30min

Avaliações

4.8

73 avaliações

5 stars
82,15%
4 stars
15,24%
3 stars
2,23%
2 stars
0,37%

Principais avaliações do FUNDAMENTALS OF MACHINE LEARNING FOR HEALTHCARE

por MM7 de out de 2020

An excellent course for professionals with healthcare background, specially for those who want to test the water before diving deep into AI in Healthcare.

por MN2 de jan de 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.

por SM21 de set de 2021

the quality of videos was great. week 4 till week 7 have some hard to learn problems, it is better to make it more clear and easier to understand.

por LA1 de abr de 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,

Visualizar todas as avaliações

Sobre Programa de cursos integrados AI in Healthcare

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.

Perguntas Frequentes – FAQ

Quando terei acesso às palestras e às tarefas?
O acesso a palestras e tarefas depende do tipo de inscrição. Se você participar de um curso como ouvinte, você poderá ver quase todo o conteúdo do curso gratuitamente. Para acessar tarefas valendo nota e obter um Certificado, você precisará adquirir a experiência do Certificado, durante ou após a participação como ouvinte. Se você não vir a opção de participar como ouvinte:
o curso pode não oferecer essa opção. Você pode experimentar um teste gratuito ou solicitar o auxílio financeiro.
Em vez disso, o curso pode oferecer 'Curso completo, sem Certificado'. Com esta opção, é possível ver todo o conteúdo do curso, enviar as avaliações necessárias e obter uma nota final. Isso também significa que você não poderá comprar uma experiência de Certificado.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Existe algum auxílio financeiro disponível?
Sim. Em programas de aprendizado selecionados, você pode solicitar auxílio financeiro ou uma bolsa de estudos caso não consiga pagar a taxa de inscrição. Se o auxílio financeiro ou bolsa de estudos estiver disponível para a seleção do seu programa de aprendizado, você encontrará um link para se inscrever na página de descrição.
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

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O que você vai aprender