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.
AI in Healthcare Capstone
This course is part of AI in Healthcare Specialization
Instructors: Matthew Lungren
16,394 already enrolled
(257 reviews)
Skills you'll gain
- Patient Evaluation
- Health Informatics
- Predictive Modeling
- Applied Machine Learning
- Regulatory Compliance
- Feature Engineering
- Application Deployment
- Clinical Data Management
- Healthcare Industry Knowledge
- Data Ethics
- Machine Learning Software
- Artificial Intelligence and Machine Learning (AI/ML)
- Performance Tuning
- Data Collection
- Machine Learning
- Health Care Procedure and Regulation
Details to know
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There are 5 modules in this course
What's included
2 readings2 assignments2 peer reviews
What's included
1 reading2 assignments2 peer reviews
What's included
1 reading2 assignments2 peer reviews
What's included
1 reading2 assignments2 peer reviews
What's included
3 readings1 assignment1 peer review
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Reviewed on Jun 20, 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.
Reviewed on Jul 6, 2024
The quizzes have errors in the answers (already reported), and peer review is buggy. Received emails saying I have feedback but unable to see. Even AI grade and feedback cannot be seen.
Reviewed on Aug 19, 2021
Very well structured course. Easy to understand for those without a backgraoud of bioinformatics. And of course great mentors
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Frequently asked questions
Dates and Duration
Original Release Date: 11/16/2023
Expiration Date: 11/16/2026
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.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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