AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
AI for Medical Prognosis
AI for Medical Prognosis
This course is part of AI for Medicine Specialization
Instructors: Pranav Rajpurkar
28,175 already enrolled
Gain insight into a topic and learn the fundamentals.
4.7
(775 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 29 hours
Learn at your own pace
97%
Most learners liked this course
What you'll learn
Walk through examples of prognostic tasks
Apply tree-based models to estimate patient survival rates
Navigate practical challenges in medicine like missing data
Details to know
Earn a career certificate
Add to your LinkedIn profile
Assessments
4 assignments
Taught in English
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This course is part of the AI for Medicine Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
Build a linear prognostic model using logistic regression, then evaluate the model by calculating the concordance index. Finally, improve the model by adding feature interactions.
What's included
11 videos3 readings1 assignment1 programming assignment4 ungraded labs
Tune decision tree and random forest models to predict the risk of a disease. Evaluate the model performance using the c-index. Identify missing data and how it may alter the data distribution, then use imputation to fill in missing data, in order to improve model performance.
What's included
15 videos1 assignment1 programming assignment3 ungraded labs
This week, you will work with data where the time that a disease occurs is a variable. Instead of predicting just the 10-year risk of a disease, you will build more flexible models that can predict the 5 year, 7 year, or 10 year risk.
What's included
16 videos1 assignment1 programming assignment2 ungraded labs
This week, you will fit a linear model, and a tree-based risk model on survival data, to customize a risk score for each patient, based on their health profile. The risk score represents the patient’s relative risk of getting a particular disease. You will then evaluate each model’s performance by implementing and using a concordance index that incorporates time to event and censored data.
What's included
24 videos4 readings1 assignment1 programming assignment3 ungraded labs
Instructors
Instructor ratings
4.8 (134 ratings)Offered by
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Frequently asked questions
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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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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