DeepLearning.AI

AI for Medical Prognosis

This course is part of AI for Medicine Specialization

Taught in English

Some content may not be translated

Pranav Rajpurkar
Bora Uyumazturk
Eddy Shyu

Instructors: Pranav Rajpurkar

26,493 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.7

(757 reviews)

|

97%

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  

Details to know

Shareable certificate

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Assessments

4 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(757 reviews)

|

97%

Intermediate level

Recommended experience

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

See how employees at top companies are mastering in-demand skills

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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
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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 quiz1 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 quiz1 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 quiz1 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 quiz1 programming assignment3 ungraded labs

Instructors

Instructor ratings
4.8 (126 ratings)
Pranav Rajpurkar
DeepLearning.AI
3 Courses75,547 learners

Offered by

DeepLearning.AI

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