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.
This course is part of the AI for Medicine Specialization
Offered By
AI for Medicine Specializationdeeplearning.aiAbout this Course
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Shareable Certificate
Earn a Certificate upon completion
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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: French, Portuguese (Brazilian), Russian, English, Spanish
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: French, Portuguese (Brazilian), Russian, English, Spanish
Offered by
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
Syllabus - What you will learn from this course
9 hours to complete
Linear prognostic models
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.
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
7 hours to complete
Prognosis with Tree-based models
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.
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
6 hours to complete
Survival Models and Time
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.
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
8 hours to complete
Build a risk model using linear and tree-based models
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.
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
Risk Factor 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
Reviews
TOP REVIEWS FROM AI FOR MEDICAL PROGNOSIS
by JDJun 4, 2020
I am a medical image analysis enthusiast. But I always wonder why I can't I combine other patient details for extending it's application. Sure this course is awesome. I really loved it !!
by BMApr 21, 2020
This course was great and more challenging that I have expected. More focus on statistics and survival data which is important for prognosis. Course has a good flow and valuable content.
by RRSep 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.
by JHMay 1, 2020
Excellent course! Real world data and robust models. Of particular value was the implementation of the SHAP feature interpretation algorithm as applied to ensemble models.
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
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.
More questions? Visit the Learner Help Center.
