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
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Offered By
AI for Medicine SpecializationDeepLearning.AIAbout this Course
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Course 2 of 3 in the
Intermediate Level
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
Approx. 31 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, 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 Learning
- Machine Learning
- time-to-event modeling
- Random Forest
- model tuning
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 3 in the
Intermediate Level
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
Approx. 31 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Offered by
DeepLearning.AI
DeepLearning.AI is an education technology company that develops a global community of AI talent.
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), 3 readings, 3 quizzes
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)
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)
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
Reviews
- 5 stars78.10%
- 4 stars16.38%
- 3 stars3.24%
- 2 stars1.55%
- 1 star0.70%
TOP REVIEWS FROM AI FOR MEDICAL PROGNOSIS
by RWApr 18, 2020
AI for Medical Prognosis gave me a panorama of machine learning models for patient survival prediction in a simple way.
by RRJan 15, 2021
after taking Deep Learning Specialization and machine learning and python on coursera things are getting better and clearer in this course.
by MSAug 3, 2020
the coding assignments were not that hard sadly, but the knowledge about techniques and methods and formulas to interpret the prognosis is very helpful
by PPJun 3, 2020
Nicely and very thought fully organized course. This will definitely help me in apply my skills at my job. Thanks DeepLEarning.ai
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.
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