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One of the top solutions for The 2019 DII National Data Science Challenge: https://sbmi.uth.edu/dii-challenge/. More details in the paper "An interpretable deep-learning model for early prediction of sepsis in the emergency department", published on Patterns 2021.

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DII-Challenge-2019

The early identification of sepsis cases.

build the env

	pip install -r requirement.txt

data preprocessing

  • creat result folder for data preprocessing results

    mkdir result
    mkdir data
    mkdir data/models
    
  • generate json files

    cd preprocessing
    python gen_master_feature.py --master-file ../file/master.csv
    python gen_feature_time.py --vital-file ../file/vital.csv				# only for task1
    python gen_vital_feature.py --vital-file ../file/vital.csv
    python gen_label_feature.py --label-file ../file/label.csv
    

train and validate the model, the best model will saved in ../data/models/

	python main.py --task case1		# for task1 case1
	python main.py --task task1		# for task1 case2
	python main.py --task task2		# for task2

You can also run the code by:

	python run.py --label-file ../file/label.csv --vital-file ../file/vital.csv --master-file  ../master.csv --task case1

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One of the top solutions for The 2019 DII National Data Science Challenge: https://sbmi.uth.edu/dii-challenge/. More details in the paper "An interpretable deep-learning model for early prediction of sepsis in the emergency department", published on Patterns 2021.

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