neural-tangjie / ntj-aimed_2_prognosis

:octocat: This repository contains the notes, codes, assignments, quizzes and other additional materials about the course "AI for Medical Prognosis" from DeepLearning.AI Coursera.

AI-for-Medicine_2_Prognosis

This repository contains the notes, codes, assignments, quizzes and other additional materials about the course "AI for Medical Prognosis" from DeepLearning.AI Coursera. Enjoy!

The notes contain the modules outlined below:

Week	Module	Gist
1-1	Introducution to prognostic model	Intro Pre-requisites and learning outcomes
1-2	What is the risk of getting a disease	Medical Prognosis Create a linear model
1-3	Prognostic model in medical practice	Examples of Prognostic Tasks Atrial Fibrillation Liver Disease Mortality Risk of Heart Disease Risk Scores, Pandas and Numpy
1-4	Representing feature interaction	Risk Score Computation Combine Features
1-5	Evaluating prognostic models	Evaluating Prognostic Models Concordant Pairs, Risk Ties, Permissible Pairs C-Index Concordance Index
A	Build and Evaluate a Linear Risk model	^_^
2-1	Tree based model	Decision Trees for Prognosis Decision Trees Dividing the Input Space Building a Decision Tree How to Fix Overfitting Decision Tree Classifier
2-2	Identifying missing data	Survival Data Different Distributions Missing Data Example Missing Completely at Random Missing at Random Missing Not at Random Missing Data and Applying a Mask
2-3	Using imputation to handle missing data	Imputation Mean Imputation Regression Imputation Calculate Imputed Values Imputation
3-1	Survival Estimates	Survival Models Survival Function Valid Survival Functions
3-2	Time to event data	Collecting Time Data When a Stroke is Not Observed Heart Attack Data Right Censoring
3-3	Estimate survival with censored data	Estimating the Survival Function Died Immediately, or Never Die Somewhere in-between Counting Patients Using Censored Data Chain Rule of Conditional Probability Deriving Survival Calculating Probabilities from the Data Comparing Estimates Kaplan Meier Estimate Kaplan Meier
Q	Survival	_
A	Survival Estimates that Vary with Time	^_^
4-1	Survival and hazard functions	Iamge segmentation Lab-MRI data and labels
4-2	Customizing risk models to individual patients	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 Hazard Function
4-3	Non-linear risk models with survival trees	Intro to Survival Trees Survival Tree Nelson Aalen Estimator Comparing Risks of Patients Mortality Score
4-4	Evaluate survival models	Evaluation of Survival Model Permissible and Non-Permissible Pairs Possible Permissible Pairs Example of Harrell's C-Index Example of Concordant Pairs Permissible Pairs with Censoring and Time
Q	9 quetions	_
A	Cox Proportional Hazards and Random Survival Forests	^_^

“Hope is like the sun, which, as we journey toward it, casts the shadow of our burden behind us.”