/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.

Primary LanguageJupyter Notebook

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.”