cohort.py

import ipywidgets as widgets
import sys
from pathlib import Path
import os
import importlib
import shutil
import time
import yaml

def task_cohort(task,mimic_path,path_benchmark, config_path):
    root_dir = path_benchmark
    config_path=path_benchmark+'/config/'+config_path
    with open(config_path) as f:
        config = yaml.safe_load(f)
    version_path = mimic_path
    version = version_path.split('/')[-1][0]
    start = time.time()
    #----------------------------------------------config----------------------------------------------------
    disease_label =  config['disease_label']
    tim = config['time']
    label = config['label']
    timeW = config['timeW']
    include=int(timeW.split()[1])
    bucket = config['bucket']
    radimp = config['radimp']
    predW = config['predW']
    diag_flag = config['diagnosis']
    out_flag = config['output']
    chart_flag = config['chart']
    proc_flag= config['proc']
    med_flag = config['meds']
    disease_filter = config['disease_filter']
    icu_no_icu = config['icu_no_icu']
    groupingICD = config['groupingICD']
    # -------------------------------------------------------------------------------------------------------------

    data_icu=icu_no_icu=="ICU"
    data_mort=label=="Mortality"
    data_admn=label=='Readmission'
    data_los=label=='Length of Stay'

    if (disease_filter=="Heart Failure"):
        icd_code='I50'
    elif (disease_filter=="CKD"):
        icd_code='N18'
    elif (disease_filter=="COPD"):
        icd_code='J44'
    elif (disease_filter=="CAD"):
        icd_code='I25'
    else:
        icd_code='No Disease Filter'

    #-----------------------------------------------EXTRACT MIMIC-----------------------------------------------------
    if version == '2':
        cohort_output = day_intervals_cohort_v22.extract_data(icu_no_icu,label,tim,icd_code, version_path,disease_label)
    elif version == '1':
        cohort_output = day_intervals_cohort.extract_data(icu_no_icu,label,tim,icd_code, version_path,disease_label)
    end = time.time()
    print("Time elapsed : ", round((end - start)/60,2),"mins")
    #----------------------------------------------FEATURES-------------------------------------------------------
    if data_icu :
        feature_selection_icu.feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
    end = time.time()
    print("Time elapsed : ", round((end - start)/60,2),"mins")
    #----------------------------------------------GROUPING-------------------------------------------------------
    group_diag=False
    group_med=False
    group_proc=False
    if data_icu:
        if diag_flag:
            group_diag=groupingICD
        feature_selection_icu.preprocess_features_icu(cohort_output, diag_flag, group_diag,False,False,False,0,0)
    end = time.time()
    print("Time elapsed : ", round((end - start)/60,2),"mins")
    #----------------------------------------------SUMMARY-------------------------------------------------------
    if data_icu:
        feature_selection_icu.generate_summary_icu(cohort_output,diag_flag,proc_flag,med_flag,out_flag,chart_flag)
    end = time.time()
    print("Time elapsed : ", round((end - start)/60,2),"mins")
    #----------------------------------------------FEATURE SELECTION---------------------------------------------

    select_diag= config['select_diag']
    select_med= config['select_med']
    select_proc=  config['select_proc']
    #select_lab= config['select_lab']
    select_out= config['select_out']
    select_chart=  config['select_chart']

    feature_selection_icu.features_selection_icu(cohort_output, diag_flag,proc_flag,med_flag,out_flag, chart_flag,select_diag,select_med,select_proc,select_out,select_chart)
    end = time.time()
    print("Time elapsed : ", round((end - start)/60,2),"mins")
    #---------------------------------------CLEANING OF FEATURES-----------------------------------------------
    thresh=0
    if data_icu:
        if chart_flag:
            outlier_removal=config['outlier_removal']
            clean_chart=outlier_removal!='No outlier detection'
            impute_outlier_chart=outlier_removal=='Impute Outlier (default:98)'
            thresh=config['outlier']
            left_thresh=config['left_outlier']
        feature_selection_icu.preprocess_features_icu(cohort_output, False, False,chart_flag,clean_chart,impute_outlier_chart,thresh,left_thresh)
    end = time.time()
    print("Time elapsed : ", round((end - start)/60,2),"mins")
    # ---------------------------------------tim-Series Representation--------------------------------------------
    if radimp == 'forward fill and mean' :
        impute='Mean'
    elif radimp =='forward fill and median':
        impute = 'Median'
    else :
        impute = False
    
    if data_icu:
        gen=data_generation_icu_modify.Generator(task,cohort_output,data_mort,data_admn,data_los,diag_flag,proc_flag,out_flag,chart_flag,med_flag,impute,include,bucket,predW)
    print("[============TASK COHORT SUCCESSFULLY CREATED============]")

if __name__ == '__main__':
    
    
    task = sys.argv[1]
    mimic_path = sys.argv[2]
    path_benchmark = sys.argv[3]
    config = sys.argv[4]
    sys.path.append(path_benchmark+'/preprocessing/day_intervals_preproc')
    sys.path.append(path_benchmark+'/utils')
    sys.path.append(path_benchmark+'/preprocessing/hosp_module_preproc')
    sys.path.append(path_benchmark+'/model')
    import day_intervals_cohort_v22
    import day_intervals_cohort
    import feature_selection_icu
    import data_generation_icu_modify
    
    task_cohort(task, mimic_path, path_benchmark, config)