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Opiniones y comentarios de aprendices correspondientes a AI in Healthcare Capstone por parte de Universidad de Stanford

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This capstone project takes you on a guided tour exploring all the concepts we have covered in the different classes up till now. We have organized this experience around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID19 seeks care with a primary care provider. We will follow the patient's journey from the lens of the data that are created at each encounter, which will bring us to a unique de-identified dataset created specially for this specialization. The data set spans EHR as well as image data and using this dataset, we will build models that enable risk-stratification decisions for our patient. We will review how the different choices you make -- such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline -- affect the care that would be recommended by the model. During this exploration, we will also discuss the regulatory as well as ethical issues that come up as we attempt to use AI to help us make better care decisions for our patient. This course will be a hands-on experience in the day of a medical data miner. 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....

Principales reseñas

AZ

16 de dic. de 2020

Getting AI specialization Stanford University is very amazing and effective to start your AI careers. Thank you for all Stanford university lecturers, Thank you Coursera for everything !

MC

20 de jun. de 2021

Amazing experience, such in depth learning to understand how to approach data problems in healthcare settings and where they can actually be of benefit.

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