DeepLearning.AI

AI for Medical Prognosis

This course is part of AI for Medicine Specialization

Pranav Rajpurkar
Bora Uyumazturk

Instructors: Pranav Rajpurkar

28,056 already enrolled

Gain insight into a topic and learn the fundamentals.
4.7

(772 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 29 hours
Learn at your own pace
97%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.7

(772 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

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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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  • Gain a foundational understanding of a subject or tool
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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 (133 ratings)
Pranav Rajpurkar
DeepLearning.AI
3 Courses81,085 learners

Offered by

DeepLearning.AI

Recommended if you're interested in Machine Learning

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4.7

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