About this Course

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Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 3 in the

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.

Approx. 30 hours to complete

English

Subtitles: English

What you will 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 will gain

Deep LearningMachine Learningtime-to-event modelingRandom Forestmodel tuning

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 3 in the

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.

Approx. 30 hours to complete

English

Subtitles: English

Offered by

deeplearning.ai logo

deeplearning.ai

Syllabus - What you will learn from this course

Week
1

Week 1

9 hours to complete

Linear prognostic models

9 hours to complete
11 videos (Total 28 min), 4 readings, 2 quizzes
11 videos
Prerequisites and Learning Outcomes1m
Medical Prognosis2m
Examples of Prognostic Tasks2m
Atrial fibrillation2m
Liver Disease Mortality2m
Risk of heart disease2m
Risk Score Computation4m
Evaluating Prognostic Models1m
Concordant Pairs, Risk Ties, Permissible Pairs2m
C-Index3m
4 readings
Connect with your mentors and fellow learners on Slack!10m
Please save your work regularly10m
About the automatic grader10m
How to refresh your workspace10m
1 practice exercise
Week 1 Quiz30m
Week
2

Week 2

7 hours to complete

Prognosis with Tree-based models

7 hours to complete
15 videos (Total 41 min)
15 videos
Decision trees1m
Dividing the input space2m
Building a decision tree2m
How to fix overfitting4m
Survival Data3m
Different distributions2m
Missing Data example2m
Missing completely at random2m
Missing at random3m
Missing not at random3m
Imputation1m
Mean Imputation4m
Regression Imputation2m
Calculate Imputed Values2m
1 practice exercise
Week 2 Quiz30m
Week
3

Week 3

6 hours to complete

Survival Models and Time

6 hours to complete
16 videos (Total 38 min)
16 videos
Survival Function2m
Valid survival functions3m
Collecting Time Data1m
When a stroke is not observed2m
Heart Attack Data2m
Right censoring1m
Estimating the survival function1m
Died immediately, or never die3m
Somewhere in-between1m
Using censored data1m
Chain rule of conditional probability2m
Deriving Survival2m
Calculating Probabilities from the Data3m
Comparing Estimates3m
Kaplan Meier Estimate2m
1 practice exercise
Week 3 Quiz30m
Week
4

Week 4

8 hours to complete

Build a risk model using linear and tree-based models

8 hours to complete
24 videos (Total 69 min), 3 readings, 2 quizzes
24 videos
Hazard3m
Survival to hazard2m
Cumulative Hazard3m
Individualized Predictions3m
Relative risk3m
Ranking patients by risk1m
Individual vs. baseline hazard2m
Smoker vs. non-smoker2m
Effect of age on hazard3m
Risk factor increase per unit increase in a variable1m
Factor Risk Increase or Decrease4m
Intro to Survival Trees4m
Survival tree5m
Nelson Aalen estimator5m
Comparing risks of patients1m
Mortality score2m
Evaluation of Survival Model3m
Permissible and Non-Permissible Pairs2m
Possible Permissible Pairs1m
Example of Harrell's C-Index3m
Example of Concordant Pairs2m
Week 4 Summary47s
Congratulations!1m
3 readings
Congratulations on finishing course 2!10m
Acknowledgements10m
Citations10m
1 practice exercise
Week 4 Quiz30m

About the AI for Medicine Specialization

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 three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization....
AI for Medicine

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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