AI-for-Medicine_3_Treatment

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

The notes contain the modules outlined below:

Week	Module	Gist
1-1	Intro	Intro Course
1-2	Randomized	Absolute Risk Reduction Randomized Control Trials Pandas for a Medical Dataset
1-3	Average Treatment Effect	Clarifications about Upcoming Causal Inference Causal Inference Average Treatment Effect Conditional Average Treatment Effect T-Learner S-Learner Model Training/Tuning Basics with Sklearn
1-4	Individualized treatment effect	Evaluate Individualized Treatment Effect C-for-benefit C-for-benefit Calculation Logistic Regression Model Interpretation
Q	Measure Treatment Effects	_
A	Estimate Treatment Effect Using ML	^_^
2-1	Question answering	Words with Multiple Meanings Define the Answer in a Text Cleaning Text
2-2	Automatic labeling	Automatic Label Extraction for Medical Imaging Synonyms for Labels Is-a Relationships for Labels Presence or Absence of a Disease BioC Format and the NegBio Library
2-3	Ecaluate automatic labeling	Evaluating Label Extraction Precision, Recall and F1 Score Evaluating on Multiple Disease Categories Preparing Input for Text Classification
Q	Information Extraction with NLP	_
A	Natural Language Entity Extraction	^_^
3-1	Feature importance	Drop Column Method Permutation Method Lab-Permutation
3-2	Individual Feature Importance	Chapley Values Combining Importances Shapley Values for all Patients
3-3	Interpret DL Models	Interpreting CNN Models Introduction to GradCAM (Part 1) Localization Maps Heat Maps GradCAM: Continuation (Part 2)
Q	ML Interpretation	!
A	ML Interpretation	$