Ementorhub

What you'll learn

Identify problems healthcare providers face that machine learning can solve

Analyze how AI affects patient care safety, quality, and research

Relate AI to the science, practice, and business of medicine

Apply the building blocks of AI to help you innovate and understand emerging technologies

Skills you'll gain

Health Systems

Unstructured Data

Artificial Intelligence

Pharmaceuticals

Applied Machine Learning

Feature Engineering

Health Technology

Clinical Research

Electronic Medical Record

Data Ethics

Clinical Data Management

Statistical Machine Learning

Specialization - 5 course series

The final course will consist of a capstone project that will take you on a guided tour exploring all the concepts we have covered in the different classes. This will be a hands-on experience following a patient's journey from the lens of the data, using a unique dataset created for this specialization.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.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Introduction to Clinical Data

Fundamentals of Machine Learning for Healthcare

Evaluations of AI Applications in Healthcare

AI in Healthcare Capstone