Este curso forma parte de Programa especializado: AI in Healthcare

4.6
estrellas
166 calificaciones

96 %

Nigam Shah +2 instructores más

10.279 ya inscrito

ofrecido por

Programa especializado: AI in HealthcareUniversidad de Stanford

Acerca de este Curso

26.898 vistas recientes

This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care.
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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Curso 2 de 5 en

Programa especializado: AI in Healthcare

Nivel principiante

Aprox. 12 horas para completar

Inglés (English)

Qué aprenderás

How to apply a framework for medical data mining
Ethical use of data in healthcare decisions
How to make use of data that may be inaccurate in systematic ways
What makes a good research question and how to construct a data mining workflow answer it

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 2 de 5 en

Programa especializado: AI in Healthcare

Nivel principiante

Aprox. 12 horas para completar

Inglés (English)

Instructores

Calificación del instructor

4,59/5 (64 calificaciones)

ofrecido por

Universidad de Stanford

Programa - Qué aprenderás en este curso

Calificación del contenido96%(1,030 calificaciones)

Semana

Semana 1

1 hora para completar

Asking and answering questions via clinical data mining

1 hora para completar

12 videos (Total 19 minutos), 2 lecturas, 3 cuestionarios

12 videos

Welcome2m

Introduction to the data mining workflow1m

Real Life Example1m

Example: Finding similar patients2m

Example: Estimating risk37s

Putting patient data on timeline51s

Revisit the data mining workflow steps1m

Types of research questions2m

Research questions suited for clinical data55s

Example: making decision to treat1m

Properties that make answering a research question useful1m

Wrap Up1m

2 lecturas

Study Guide Module 15m

Citations and Additional Readings5m

3 ejercicios de práctica

Reflection Exercise10m

Knowledge Check30m

Semana

Semana 2

2 horas para completar

Data available from Healthcare systems

2 horas para completar

16 videos (Total 32 minutos), 3 lecturas, 4 cuestionarios

16 videos

Review of the healthcare system31s

Review of key entities and the data they collect1m

Actors with different interests2m

Common data types in Healthcare2m

Strengths and weaknesses of observational data2m

Bias and error from the healthcare system perspective2m

Bias and error of exposures and outcomes50s

How a patient's exposure might be misclassified2m

How a patient's outcome could be misclassified2m

Electronic medical record data2m

Claims data2m

Pharmacy55s

Surveillance datasets and Registries1m

Population health data sets3m

A framework to assess if a data source is useful1m

Wrap Up1m

3 lecturas

Video Image Credit

Study Guide Module 25m

Citations and Additional Readings5m

4 ejercicios de práctica

Reflection Exercise10m

Reflection Exercise15m

Knowledge Check30m

Semana

Semana 3

1 hora para completar

Representing time, and timing of events, for clinical data mining

1 hora para completar

12 videos (Total 20 minutos), 2 lecturas, 3 cuestionarios

12 videos

Introduction37s

Time, timelines, timescales and representations of time2m

Timescale: Choosing the relevant units of time29s

What affects the timescale1m

Representation of time57s

Time series and non-time series data2m

Order of events1m

Implicit representations of time1m

Different ways to put data in bins2m

Timing of exposures and outcomes3m

Clinical processes are non-stationary2m

Wrap Up1m

2 lecturas

Study Guide Module 35m

Citations and Additional Readings5m

3 ejercicios de práctica

Reflection Exercise10m

Reflection Exercise 215m

Knowledge Check30m

Semana

Semana 4

2 horas para completar

Creating analysis ready datasets from patient timelines

2 horas para completar

18 videos (Total 33 minutos), 2 lecturas, 3 cuestionarios

18 videos

Turning clinical data into something you can analyze1m

Defining the unit of analysis52s

Using features and the presence of features2m

How to create features from structured sources1m

Standardizing features52s

Dealing with too many features3m

The origins of missing values3m

Dealing with missing values1m

Summary recommendations for missing values1m

Constructing new features1m

Examples of engineered features2m

When to consider engineered features1m

Main points about creating analysis ready datasets53s

Structured knowledge graphs2m

So what exactly is in a knowledge graph2m

What are important knowledge graphs2m

How to choose which knowledge graph to use1m

Wrap Up45s

2 lecturas

Study Guide Module 45m

Citations and Additional Readings5m

3 ejercicios de práctica

Reflection Exercise10m

Reflection Exercise20m

Knowledge Check30m

Semana

Semana 5

2 horas para completar

Handling unstructured healthcare data: text, images, signals

2 horas para completar

19 videos (Total 30 minutos), 4 lecturas, 3 cuestionarios

19 videos

Introduction to unstructured data43s

What is clinical text1m

The value of clinical text2m

What makes clinical text difficult to handle2m

Privacy and de-identification3m

A primer on Natural Language Processing1m

Practical approach to processing clinical text5m

Summary - Clinical text33s

Overview and goals of medical imaging1m

Why are images important?23s

What are images?3m

A typical image management process1m

Summary - Images36s

Overview of biomedical signals28s

Why are signals important?45s

What are signals?54s

What are the major issues with using signals?1m

Summary - Signals35s

Wrap Up39s

4 lecturas

Video Image Credit

Study Guide Module 55m

Citations and Additional Readings5m

3 ejercicios de práctica

Reflection Exercise30m

Reflection Exercise10m

Knowledge Check30m

Semana

Semana 6

1 hora para completar

Putting the pieces together: Electronic phenotyping

1 hora para completar

11 videos (Total 18 minutos), 3 lecturas, 3 cuestionarios

11 videos

Introduction to electronic phenotyping1m

Challenges in electronic phenotyping1m

Specifying an electronic phenotype2m

Two approaches to phenotyping40s

Rule-based electronic phenotyping1m

Examples of rule based electronic phenotype definitions1m

Constructing a rule based phenotype definition1m

Probabilistic phenotyping1m

Approaches for creating a probabilistic phenotype definition2m

Software for probabilistic phenotype definitions34s

Wrap Up2m

3 lecturas

Video Image Credit

Study Guide Module 65m

Citations and Additional Readings5m

3 ejercicios de práctica

Reflection Exercise10m

Knowledge Check30m

Semana

Semana 7

1 hora para completar

Ethics

1 hora para completar

7 videos (Total 46 minutos), 2 lecturas

Semana

Semana 8

1 hora para completar

Course Conclusion

1 hora para completar

1 video (Total 2 minutos), 3 lecturas, 1 cuestionario

Reseñas

4.6

33 reseña

5 stars
69,71 %
4 stars
24,57 %
3 stars
4 %
2 stars
1,71 %

Principales reseñas sobre INTRODUCTION TO CLINICAL DATA

por YX4 de nov. de 2020

Very clear and well-organized course. I have learned quite a bit about the different types of clinical data, why they are important, and how to transfer them to analytical useable data sets.

por LO31 de may. de 2022

Good introductory course, although I must admit I was expecting a little bit a more hands-on approach. Some instructors speak very fast, so I had to keep replaying the video.

por AM27 de may. de 2022

Course was generally good -- final assessment questions were not always carefully worded nor proofread.

por KS16 de oct. de 2020

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

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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Introduction to Clinical Data

Acerca de este Curso

Qué aprenderás

Instructores

Nigam Shah

Steven Bagley

David Magnus

ofrecido por

Universidad de Stanford

Programa - Qué aprenderás en este curso

Asking and answering questions via clinical data mining

Data available from Healthcare systems

Representing time, and timing of events, for clinical data mining

Creating analysis ready datasets from patient timelines

Handling unstructured healthcare data: text, images, signals

Putting the pieces together: Electronic phenotyping

Ethics

Course Conclusion

Reseñas

Principales reseñas sobre INTRODUCTION TO CLINICAL DATA

Acerca de Programa especializado: AI in Healthcare

Preguntas Frecuentes

Comienza o impulsa tu carrera profesional

Buscar temas populares

Cursos y artículos populares

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Introduction to Clinical Data

Acerca de este Curso

Qué aprenderás

Instructores

Nigam Shah

Steven Bagley

David Magnus

ofrecido por

Universidad de Stanford

Programa - Qué aprenderás en este curso

Asking and answering questions via clinical data mining

Data available from Healthcare systems

Representing time, and timing of events, for clinical data mining

Creating analysis ready datasets from patient timelines

Handling unstructured healthcare data: text, images, signals

Putting the pieces together: Electronic phenotyping

Ethics

Course Conclusion

Reseñas

Principales reseñas sobre INTRODUCTION TO CLINICAL DATA

Acerca de Programa especializado: AI in Healthcare

Preguntas Frecuentes

Coursera Footer

Comienza o impulsa tu carrera profesional

Buscar temas populares

Cursos y artículos populares

Obtén un título de grado o un certificado en línea

Coursera

Comunidad

Más