【AI-for-Medical-Treatment】

★Project1:Estimating Treatment Effect Using Machine Learning

1. Aanalyzing data from a randomized control trial using both:
   -Traditional statistical methods and the more recent machine learning techniques
   
2. Interpreting Multivariate Models
   -Quantifying treatment effect
   -Calculating baseline risk
   -Calculating predicted risk reduction
   
3. Evaluating Treatment Effect Models
   -Comparing predicted and empirical risk reductions
   -Computing C-statistic-for-benefit
   
4. Interpreting ML models for Treatment Effect Estimation
   -Implement T-learner
   

★Project2:Natural Language Entity Extraction

1. Extracting disease labels from clinical reports
   -Text matching
   -Evaluating a labeler
   -Negation detection
   -Dependency parsing
   
2. Question Answering with BERT
   -Preprocessing text for input
   -Extracting answers from model output
   

★Project3:ML Interpretation

1. Interpreting Deep Learning Models
   -Understanding output using GradCAMs
   
2. Feature Importance in Machine Learning
   -Permutation Method
   -SHAP Values