Este curso forma parte de Programa especializado: AI in Healthcare

4.8
estrellas
269 calificaciones

96 %

Matthew Lungren +1 instructores más

13.103 ya inscrito

Ofrecido Por

Programa especializado: AI in HealthcareUniversidad de Stanford

Acerca de este Curso

35.443 vistas recientes

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.

Fechas límite flexibles

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100 % en línea

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Curso 3 de 5 en

Programa especializado: AI in Healthcare

Nivel principiante

Aprox. 12 horas para completar

Inglés (English)

Qué aprenderás

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.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 3 de 5 en

Programa especializado: AI in Healthcare

Nivel principiante

Aprox. 12 horas para completar

Inglés (English)

Instructores

Calificación del instructor

4,9/5 (111 calificaciones)

Matthew Lungren

Associate Professor

Department of Radiology

14.453 alumnos

2 cursos

Serena Yeung

Assistant Professor

Biomedical Data Science

14.453 alumnos

2 cursos

Ofrecido por

Universidad de Stanford

Programa - Qué aprenderás en este curso

Calificación del contenido96%(1,334 calificaciones)

Semana

Semana 1

2 horas para completar

Why machine learning in healthcare?

2 horas para completar

7 videos (Total 34 minutos), 6 lecturas, 3 cuestionarios

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 lecturas

Getting Started: Creators of This Course5m

Video Image Credit

Study Guide Module 15m

Citations and Additional Readings10m

Video Image Credit

3 ejercicios de práctica

Reflection Exercise15m

Reflection Exercise10m

Knowledge Check30m

Semana

Semana 2

2 horas para completar

Concepts and Principles of machine learning in healthcare part 1

2 horas para completar

8 videos (Total 51 minutos), 2 lecturas, 3 cuestionarios

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 lecturas

Study Guide Module 25m

Citations and Additional Readings5m

3 ejercicios de práctica

Reflection Exercise10m

Knowledge Check30m

Semana

Semana 3

2 horas para completar

Concepts and Principles of machine learning in healthcare part 2

2 horas para completar

10 videos (Total 63 minutos), 3 lecturas, 3 cuestionarios

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 lecturas

Video Image Credit

Study Guide Module 35m

Citations and Additional Readings5m

3 ejercicios de práctica

Reflection Exercise10m

Knowledge Check30m

Semana

Semana 4

2 horas para completar

Evaluation and Metrics for machine learning in healthcare

2 horas para completar

5 videos (Total 35 minutos), 2 lecturas, 3 cuestionarios

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 lecturas

Study Guide Module 45m

Citations and Additional Readings5m

3 ejercicios de práctica

Reflection Exercise 110m

Reflection Exercise 210m

Knowledge Check30m

Reseñas

4.8

73 reseña

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

Principales reseñas sobre FUNDAMENTALS OF MACHINE LEARNING FOR HEALTHCARE

por MM7 de oct. 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 AJ8 de sep. de 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.

por BH31 de ago. de 2022

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

por SR9 de oct. de 2020

Outstanding teaching and pacing by both professors and an excellent generalized instruction of ML for healthcare.

Ver todas las reseñas

Acerca de Programa especializado: 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.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.
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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Qué aprenderás