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# 10 t-SNE.ipynb
# 10 t-SNE.ipynb
 
 
# 11_COMMENTED_Machine_learning_algorithms_whole_note_features_comparison.ipynb
# 11_COMMENTED_Machine_learning_algorithms_whole_note_features_comparison.ipynb
 
 
# 14 Download MIDAS UNlabelled data from aws s3.ipynb
# 14 Download MIDAS UNlabelled data from aws s3.ipynb
 
 
# 2 Download MIDAS labelled data from aws s3.ipynb
# 2 Download MIDAS labelled data from aws s3.ipynb
 
 
#1 - Set Up Folders.ipynb
#1 - Set Up Folders.ipynb
 
 
#11 SVD_whole_note.ipynb
#11 SVD_whole_note.ipynb
 
 
#12 XGBoost_ctv_whole_note (1).ipynb
#12 XGBoost_ctv_whole_note (1).ipynb
 
 
#12 XGBoost_ctv_whole_note.ipynb
#12 XGBoost_ctv_whole_note.ipynb
 
 
#13 Make_predictions_XGBoost_tfidf_whole_note.ipynb
#13 Make_predictions_XGBoost_tfidf_whole_note.ipynb
 
 
#14 XGBoost_ctv_snippet.ipynb
#14 XGBoost_ctv_snippet.ipynb
 
 
#15 XGBoost_tfidf_svd_whole_note.ipynb
#15 XGBoost_tfidf_svd_whole_note.ipynb
 
 
#16 Make_predictions_XGBoost_ctv_snippet.ipynb
#16 Make_predictions_XGBoost_ctv_snippet.ipynb
 
 
#17 Make_predictions_XGBoost_ctv_whole_note.ipynb
#17 Make_predictions_XGBoost_ctv_whole_note.ipynb
 
 
#18 midas_labelled_data_labeling accuracy.ipynb
#18 midas_labelled_data_labeling accuracy.ipynb
 
 
#20 EDA_midas_labelled_data.ipynb
#20 EDA_midas_labelled_data.ipynb
 
 
#21 EDA_midas_UNlabelled_data.ipynb
#21 EDA_midas_UNlabelled_data.ipynb
 
 
#22 XGBoost_ctv_whole_note_miscalssification.ipynb
#22 XGBoost_ctv_whole_note_miscalssification.ipynb
 
 
#3 Word_Distribution in the labelled data.ipynb
#3 Word_Distribution in the labelled data.ipynb
 
 
#4 NB and random fores.ipynb
#4 NB and random fores.ipynb
 
 
#5 Data Viz.ipynb
#5 Data Viz.ipynb
 
 
#6 Machine_learning_whole_note (1).ipynb
#6 Machine_learning_whole_note (1).ipynb
 
 
#6 Machine_learning_whole_note.ipynb
#6 Machine_learning_whole_note.ipynb
 
 
#7 Machine_learning_snippet (1).ipynb
#7 Machine_learning_snippet (1).ipynb
 
 
#7 Machine_learning_snippet.ipynb
#7 Machine_learning_snippet.ipynb
 
 
#8 XGBoost_tfidf_whole_note.ipynb
#8 XGBoost_tfidf_whole_note.ipynb
 
 
#8 XGBoost_whole_note.ipynb
#8 XGBoost_whole_note.ipynb
 
 
#9 Template.ipynb
#9 Template.ipynb
 
 
AWS.ipynb
AWS.ipynb
 
 
BERT_model1.ipynb
BERT_model1.ipynb
 
 
BERT_ori.ipynb
BERT_ori.ipynb
 
 
BERT_prepare_prediction_table.ipynb
BERT_prepare_prediction_table.ipynb
 
 
MIDAS_Split_Labeled_data_to_classes.ipynb
MIDAS_Split_Labeled_data_to_classes.ipynb
 
 
MIDAS_custom_packages.ipynb
MIDAS_custom_packages.ipynb
 
 
Midas_Deep_Learning.ipynb
Midas_Deep_Learning.ipynb
 
 
Midas_Deep_Learning_data_with_common_words.ipynb
Midas_Deep_Learning_data_with_common_words.ipynb
 
 
Midas_Deep_Learning_data_without_common_words.ipynb
Midas_Deep_Learning_data_without_common_words.ipynb
 
 
PCA.ipynb
PCA.ipynb
 
 
README.md
README.md
 
 

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Classification of doctor's notes with NLP

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NLP pipeline that reads free-text physician notes and predicts a patient's MIDAS migraine-disability grade — comparing TF-IDF/XGBoost, deep learning, and fine-tuned BERT.

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