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
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.
Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, you’ll walk through multiple examples of prognostic tasks. You’ll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you’ll learn how to handle missing data, a key real-world challenge.
These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. This course focuses on tree-based machine learning, so a foundation in deep learning is not required for this course. However, a foundation in deep learning is highly recommended for course 1 and 3 of this specialization. You can gain a foundation in deep learning by taking the Deep Learning Specialization offered by deeplearning.ai and taught by Andrew Ng.
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.
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!•2 minutes
(Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace•5 minutes
About the AutoGrader•10 minutes
1 assignment•Total 30 minutes
Prognostic Models•30 minutes
1 programming assignment•Total 180 minutes
Build and Evaluate a Linear Risk model•180 minutes
4 ungraded labs•Total 240 minutes
Create a Linear Model•60 minutes
Risk Scores, Pandas and Numpy•60 minutes
Combine Features•60 minutes
Concordance Index•60 minutes
Prognosis with Tree-based Models
Week 2•7 hours to complete
Module details
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.
Decision Trees, Missing Data and Imputation•30 minutes
1 programming assignment•Total 180 minutes
Risk Models Using Tree-based Models•180 minutes
3 ungraded labs•Total 180 minutes
Decision Tree Classifier•60 minutes
Missing Data and Applying a Mask•60 minutes
Imputation•60 minutes
Survival Models and Time
Week 3•6 hours to complete
Module details
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.
Survival Estimates that Vary with Time•180 minutes
2 ungraded labs•Total 120 minutes
Counting Patients•60 minutes
Kaplan Meier•60 minutes
Build a Risk Model Using Linear and Tree-based Models
Week 4•8 hours to complete
Module details
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.
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RR
5·
Reviewed on Sep 8, 2020
This course is one of the best courses to learn about Medical Prognosis. Really, the survival models were described in great detail. Thank you, Pranav for this wonderful course.
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RW
5·
Reviewed on Apr 18, 2020
AI for Medical Prognosis gave me a panorama of machine learning models for patient survival prediction in a simple way.
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RR
5·
Reviewed on Jan 15, 2021
after taking Deep Learning Specialization and machine learning and python on coursera things are getting better and clearer in this course.
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What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.