Course Summary
This course introduces students to the application of AI in medical prognosis. It covers various tools and techniques that can be used to predict the outcomes of medical conditions and treatments.Key Learning Points
- Understand the basics of AI in medical prognosis
- Learn how to use machine learning tools for medical data analysis
- Discover the latest research in AI for medical prognosis
Related Topics for further study
Learning Outcomes
- Develop expertise in AI for medical prognosis
- Gain practical experience in machine learning tools for medical data analysis
- Apply predictive modeling techniques to medical prognosis
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics and probability
- Familiarity with programming languages such as Python or R
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Interactive quizzes
Similar Courses
- AI in Healthcare Specialization
- Data Science in Healthcare
Related Education Paths
Notable People in This Field
- Director, Scripps Translational Science Institute
- Co-director, Stanford Institute for Human-Centered AI
Related Books
Description
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.
Knowledge
- Walk through examples of prognostic tasks
- Apply tree-based models to estimate patient survival rates
- Navigate practical challenges in medicine like missing data Â
Outline
- 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
Summary of User Reviews
Discover how artificial intelligence is revolutionizing medical prognosis. This course received high praise from learners for its informative content and practical applications in the healthcare industry.Key Aspect Users Liked About This Course
The course provides practical applications of AI in medical prognosis, giving learners a better understanding of how it can be used in the healthcare industry.Pros from User Reviews
- Informative content on artificial intelligence and its applications in medical prognosis
- Practical examples of how AI can be used in the healthcare industry
- Well-structured and easy to follow course material
- Engaging and knowledgeable instructors
- Great opportunity to develop new skills and knowledge
Cons from User Reviews
- Some concepts may be challenging for beginners
- Limited scope of the course
- Not enough hands-on exercises
- Some learners wish there were more case studies presented
- Course may be too technical for some learners
Pranav Rajpurkar
4.7 Raiting
Complete Machine Learning & Data Science Bootcamp 2021
- 4.6
-
![]()
![]()
![]()
![]()
![]()
Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more! You will go from zero to mastery!...
Python For Vision and Detection : OpenCv Python
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Learn Vision Includes all OpenCV Image Processing Features with Simple Examples. Face Detection, Face Recognition Welcome to the ultimate online course on Python for Computer Vision!...
Introduction to Longevity Medicine
- 4.4
-
![]()
![]()
![]()
![]()
![]()
A Medical Guide to Longevity Medicine for Physicians The paradigm of longevity and healthy aging as the top priority will undoubtedly greatly impact the primary, secondary and tertiary prevention rates....
Fundamentals of Machine Learning for Healthcare
- 4.8
-
![]()
![]()
![]()
![]()
![]()
Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. Co-author: Geoffrey Angus...
