ICU Challenge

In this project we classified on 7 tasks using an XGBoost Classifier and also printed the feature importance so as to see which features help in the classification.

Instructions on how to run the project.

• Create a virtualenvironment and install requirements.txt

• Example usage of an argparser

   -> % python code/main.py -h
   usage: main.py [-h] [-f {tune,train_eval}] [-d DATA] [-l LABELS]
   
   optional arguments:
     -h, --help            show this help message and exit
     -f {tune,train_eval}, --function {tune,train_eval}
                           choose the function of the code
     -d DATA, --data DATA  choose the folder where the data is
     -l LABELS, --labels LABELS
                           choose the folder where the labels are
  

• An example usage is (takes about 5-10 minutes for the full output):

   python code/main.py -f train_eval -d /path/to/data -l /path/to/labels
  

• An example output where we can see our final classification scores as well as the most important features:

  	Low SA02
    Most important feature, Oxygen saturation in timestep 2
    Low heartrate
    Most important feature, Respiratory rate in timestep 0
    Low respiration
    Most important feature, Heart rate in timestep 1
    Low Systemic Mean
    Most important feature, Heart rate in timestep 1
    High Heartrate
    Most important feature, Respiratory rate in timestep 0
    High respiration
    Most important feature, Respiratory rate in timestep 1
    High Systemic Mean
    Most important feature, Heart rate in timestep 2
    +--------------------+--------------------+--------------------+
    |        Task        |       AUROC        |       AURPC        |
    +--------------------+--------------------+--------------------+
    |      Low SA02      | 0.9912229148357453 | 0.991762160142757  |
    |   Low heartrate    | 0.9924621118928509 | 0.9925487250898325 |
    |  Low respiration   | 0.9896811148978384 | 0.9889808977160514 |
    | Low Systemic Mean  | 0.988212057593581  | 0.9906099666233024 |
    |   High Heartrate   | 0.9918989261768103 | 0.9891691887319317 |
    |  High respiration  | 0.9539927285772434 | 0.9551514355480142 |
    | High Systemic Mean | 0.9930555555555556 | 0.9934091623448059 |
    +--------------------+--------------------+--------------------+
  

• If you want you can also run the gridsearch to see why we chose these hyperparameters (but it will take 2-3 days on a single core): python code/main.py -f tune -d /path/to/data -l /path/to/labels