AI for Medical Prognosis

This course is part of AI for Medicine Specialization

Pranav Rajpurkar
Bora Uyumazturk
Eddy Shyu

Instructors: Pranav Rajpurkar

22,701 already enrolled


Gain insight into a topic and learn the fundamentals


(727 reviews)

Intermediate level

Recommended experience

29 hours (approximately)
Flexible schedule
Learn at your own pace

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  

Skills you'll gain

  • Category: Deep Learning
  • Category: Machine Learning
  • Category: time-to-event modeling
  • Category: Random Forest
  • Category: model tuning

Details to know

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Quizzes and assessments

4 quizzes, 0 assessments

Subtitles: Arabic, French,


Gain insight into a topic and learn the fundamentals


(727 reviews)

Intermediate level

Recommended experience

29 hours (approximately)
Flexible schedule
Learn at your own pace

Build your subject-matter expertise

This course is part of the AI for Medicine Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

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 quiz

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 quiz

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 quiz

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 videos3 readings1 quiz


Instructor ratings
4.7 (121 ratings)
Pranav Rajpurkar
3 Courses63,276 learners

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