This course is part of the AI for Medicine Specialization

Pranav Rajpurkar +2 more instructors

Offered By

AI for Medicine SpecializationDeepLearning.AI

About this Course

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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.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Course 2 of 3 in the

AI for Medicine Specialization

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: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish

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

AI for Medicine Specialization

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: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish

Instructors

Instructor rating

4.7/5 (83 Ratings)

Offered by

DeepLearning.AI

Founded by Andrew Ng, DeepLearning.AI is an education technology company that develops a global community of AI talent.

Syllabus - What you will learn from this course

Content Rating97%(2,060 ratings)

Week

Week 1

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

Course 2 Intro with Andrew and Pranav2m

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

Week 2

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 trees for prognosis1m

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

Week 3

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 models39s

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

Week 4

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

Hazard Functions51s

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

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.

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.
Will I earn university credit for completing the Course?
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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AI for Medical Prognosis