DeepLearning.AI - AI for Medical Prognosis
- Offered byCoursera
AI for Medical Prognosis at Coursera Overview
Duration | 30 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
AI for Medical Prognosis at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
AI for Medical Prognosis at Coursera Course details
- 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.
AI for Medical Prognosis at Coursera Curriculum
Linear prognostic models
Course 2 Intro with Andrew and Pranav
Prerequisites and Learning Outcomes
Medical Prognosis
Examples of Prognostic Tasks
Atrial fibrillation
Liver Disease Mortality
Risk of heart disease
Risk Score Computation
Evaluating Prognostic Models
Concordant Pairs, Risk Ties, Permissible Pairs
C-Index
Connect with your mentors and fellow learners on Slack!
Please save your work regularly
About the automatic grader
How to refresh your workspace
Week 1 Quiz
Prognosis with Tree-based models
Decision trees for prognosis
Decision trees
Dividing the input space
Building a decision tree
How to fix overfitting
Survival Data
Different distributions
Missing Data example
Missing completely at random
Missing at random
Missing not at random
Imputation
Mean Imputation
Regression Imputation
Calculate Imputed Values
Week 2 Quiz
Survival Models and Time
Survival models
Survival Function
Valid survival functions
Collecting Time Data
When a stroke is not observed
Heart Attack Data
Right censoring
Estimating the survival function
Died immediately, or never die
Somewhere in-between
Using censored data
Chain rule of conditional probability
Deriving Survival
Calculating Probabilities from the Data
Comparing Estimates
Kaplan Meier Estimate
Week 3 Quiz
Build a risk model using linear and tree-based models
Hazard Functions
Hazard
Survival to hazard
Cumulative Hazard
Individualized Predictions
Relative risk
Ranking patients by risk
Individual vs. baseline hazard
Smoker vs. non-smoker
Effect of age on hazard
Risk factor increase per unit increase in a variable
Risk Factor Increase or Decrease
Intro to Survival Trees
Survival tree
Nelson Aalen estimator
Comparing risks of patients
Mortality score
Evaluation of Survival Model
Permissible and Non-Permissible Pairs
Possible Permissible Pairs
Example of Harrell's C-Index
Example of Concordant Pairs
Week 4 Summary
Congratulations!
Congratulations on finishing course 2!
Acknowledgements
Citations
Week 4 Quiz
AI for Medical Prognosis at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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